|
1
|
Lockwich J, Kitzman P, Skubik-Peplaski C, Andreatta R, Schwartzkopf-Phifer K. Pushing the limit to reach meaningful change: the impact of intensity-driven exercise on clinical outcomes for individuals with Parkinson's disease. A single-subject design. Disabil Rehabil 2025; 47:2009-2016. [PMID: 39126138 DOI: 10.1080/09638288.2024.2388873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 07/31/2024] [Accepted: 08/01/2024] [Indexed: 08/12/2024]
Abstract
PURPOSE Parkinson's disease creates an inability to perform previous learned autonomic tasks, such as walking, which worsens with disease progression. Recommendations to incorporate exercise at moderate to high intensities for this population has been established but there is limited knowledge about its impact on clinical based outcomes. The purpose of this research is to investigate the effectiveness of a 6-week intensity-driven walking program on clinical-based outcomes in individuals with PD. MATERIALS/METHODS Five individuals with PD were recruited for this single-subject withdrawal design (A-B-A-B) study. 6-minute walk performance and other core neurological measures of gait were collected. Intervention phases incorporated a 30-minute individualized intensity-driven treadmill walking program practiced at 65% or more of ones maximum heart rate. Increased treadmill speed, incline, and resistance were manipulated to reach the target heart rate zone. RESULTS 6-minute walk test within condition visual analysis demonstrated a therapeutic change during intervention phases and a countertherapeutic change during withdraw periods for all 5 individuals. An abrupt therapeutic effect was demonstrated for all individuals between conditions with the percent of nonoverlapping data ranging from 70-90%. Band method analysis revealed a range of 9-19 sessions two standard deviations above baseline mean performances for all individuals. CONCLUSION To achieve sufficient walking performance, gait practiced at higher intensity levels may provide the optimal solution as an adjunct to standard care for individuals with PD who want to improve their walking.
Collapse
Affiliation(s)
- J Lockwich
- Department of Physical Therapy, Duquesne University, Pittsburgh, PA, USA
| | - P Kitzman
- Department of Rehabilitation Sciences, University of KY, Lexington, KY, USA
| | - C Skubik-Peplaski
- Department of Occupational Science and Occupational Therapy, Eastern Kentucky University, Richmond, KY, USA
| | - R Andreatta
- Department of Rehabilitation Sciences, University of KY, Lexington, KY, USA
| | | |
Collapse
|
|
2
|
Song Z, Ou J, Wu S, Shu L, Fu Q, Xu X. Wearable fall risk assessment by discriminating recessive weak foot individual. J Neuroeng Rehabil 2025; 22:64. [PMID: 40114179 PMCID: PMC11924617 DOI: 10.1186/s12984-025-01599-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 03/02/2025] [Indexed: 03/22/2025] Open
Abstract
BACKGROUND Sensor-based technologies have been widely used in fall risk assessment. To enhance the model's robustness and reliability, it is crucial to analyze and discuss the factors contributing to the misclassification of certain individuals, enabling purposeful and interpretable refinement. METHODS This study identified an abnormal gait pattern termed "Recessive weak foot (RWF)," characterized by a discontinuous high-risk gait on the weak foot side, observed through weak foot feature space. This condition negatively affected the training and performance of fall risk assessment models. To address this, we proposed a trainable threshold method to discriminate individuals with this pattern, thereby enhancing the model's generalization performance. We conducted feasibility and ablation studies on two self-established datasets and tested the compatibility on two published gait-related Parkinson's disease (PD) datasets. RESULTS Guided by a customized index and the optimized adaptive thresholds, our method effectively screened out the RWF individuals. Specifically, after fine adaptation, the individual-specific models could achieve accuracies of 87.5% and 73.6% on an enhanced dataset. Compared to the baseline, the proposed two-stage model demonstrated improved performance, with an accuracy of 85.4% and sensitivity of 87.5%. In PD dataset, our method mitigated potential overfitting from low feature dimensions, increasing accuracy by 4.7%. CONCLUSIONS Our results indicate the proposed method enhanced model generalization by allowing the model to account for individual differences in gait patterns and served as an effective tool for quality control, helping to reduce misdiagnosis. The identification of the RWF gait pattern prompted connections to related studies and theories, suggesting avenues for further research. Future investigations are needed to further explore the implications of this gait pattern and verify the method's compatibility.
Collapse
Affiliation(s)
- Zhen Song
- School of Microelectronics, and School of EIE, South China University of Technology, Guangzhou, 510641, China
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jianlin Ou
- The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China
| | - Shibin Wu
- School of Microelectronics, and School of EIE, South China University of Technology, Guangzhou, 510641, China
| | - Lin Shu
- School of Microelectronics, and School of EIE, South China University of Technology, Guangzhou, 510641, China.
- Pazhou Lab, Guangzhou, 510330, China.
| | - Qihan Fu
- Department of Physics, Rensselear Polytechnic Institute, Troy, NY, 12180, USA
| | - Xiangmin Xu
- School of Microelectronics, and School of EIE, South China University of Technology, Guangzhou, 510641, China
- Institute of Modern Industrial Technology of SCUT in Zhongshan, Zhongshan, 528400, China
| |
Collapse
|
|
3
|
Daadi MM, Snyder EY. Exercise your graft - An important lesson for cell replacement therapy for Parkinson's disease. Exp Neurol 2025; 385:115085. [PMID: 39631719 DOI: 10.1016/j.expneurol.2024.115085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Revised: 08/24/2024] [Accepted: 11/27/2024] [Indexed: 12/07/2024]
Abstract
Parkinson's disease (PD) is a complex multisystem, chronic and so far, incurable disease affecting millions of people worldwide. With the continuing need for better therapeutic options for PD, there is a global renewed interest in cell replacement therapy due to progress in using pluripotent stem cells as an unlimited source of dopaminergic (DA) neurons for cell transplantation. Despite the significant progress made, obstacles remain that interfere with the restoration of functional circuits by DA grafts. The functional connectivity between DA grafts and host cells may be enhanced by adjunctive therapies, such as physical activity. Exercise modalities, such as use of treadmill, enhance neuroplasticity and improve motor and cognitive functions in PD patients. The patients are able to re-learn movement and adjust their posture, which, in turn, results in short term-reduced rigidity and improved stride length and cadence. By stabilizing selected active inputs and eliminating inactive ones, activity-dependent mechanisms fine-tune new neural circuits for optimal connection and physiological function. This communication will review the mechanisms and synergies between cell replacement therapy and physical and cognitive training to enhance induced pluripotent stem cell-mediated functional reinnervation of the striatum in PD.
Collapse
Affiliation(s)
- Marcel M Daadi
- Department of Cell Systems & Anatomy, Long School of Medicine, University of Texas Health at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229, USA; Department of Radiology, Long School of Medicine, University of Texas Health at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229, USA.
| | - Evan Y Snyder
- Sanford Consortium for Regenerative Medicine, La Jolla, CA 92037, USA; Sanford Burnham Prebys Medical Discovery Institute, Center for Stem Cells & Regenerative Medicine, La Jolla, CA 92037, USA
| |
Collapse
|
|
4
|
Shimotori D, Aimoto K, Otaka E, Matsumura J, Tanaka S, Kagaya H, Kondo I. Influence of treadmill speed selection on gait parameters compared to overground walking in subacute rehabilitation patients. J Phys Ther Sci 2025; 37:89-94. [PMID: 39902307 PMCID: PMC11787860 DOI: 10.1589/jpts.37.89] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2024] [Accepted: 12/03/2024] [Indexed: 02/05/2025] Open
Abstract
[Purpose] Treadmill-based interventions are widely utilized in rehabilitation due to their advantages of providing controlled environments and enabling individualized training. However, the differences between overground and treadmill walking during the subacute rehabilitation phase remain incompletely understood. This study aimed to compare gait parameters between treadmill walking at varying speeds and overground walking in a subacute rehabilitation setting. [Participants and Methods] A total of 42 inpatients with cerebrovascular and orthopedic conditions were recruited from a convalescent rehabilitation ward. Gait parameters were measured using the Gait Real-time Analysis Interactive Lab (GRAIL) system during comfortable overground walking and treadmill walking at various speeds, including self-selected comfortable speeds and speeds matched to overground walking. Walking speed, stride length, cadence, and step width were calculated without markers and compared across conditions. [Results] The comfortable treadmill walking speed was significantly lower than the overground walking speed (mean [standard deviation]: 0.85 [0.23] m/s vs. 1.20 [0.20] m/s). Stride length was significantly shorter during treadmill walking at comfortable speeds compared to overground walking (0.86 [0.22] m vs. 1.21 [0.18] m), whereas step width was significantly wider (0.17 [0.04] m vs. 0.13 [0.03] m). [Conclusion] Maintaining cadence at reduced treadmill speeds promotes comfortable endurance training in subacute rehabilitation patients.
Collapse
Affiliation(s)
- Daiki Shimotori
- Laboratory for Practical Technology in Community, Assistive
Robot Center, National Center for Geriatrics and Gerontology: 7-430 Morioka, Obu, Aichi
474-8511, Japan
| | - Keita Aimoto
- Department of Rehabilitation Medicine, National Center for
Geriatrics and Gerontology, Japan
| | - Eri Otaka
- Laboratory for Practical Technology in Community, Assistive
Robot Center, National Center for Geriatrics and Gerontology: 7-430 Morioka, Obu, Aichi
474-8511, Japan
| | - Jun Matsumura
- Department of Rehabilitation Medicine, National Center for
Geriatrics and Gerontology, Japan
| | - Shintaro Tanaka
- Department of Rehabilitation Medicine, National Center for
Geriatrics and Gerontology, Japan
| | - Hitoshi Kagaya
- Department of Rehabilitation Medicine, National Center for
Geriatrics and Gerontology, Japan
| | - Izumi Kondo
- Assistive Robot Center, National Center for Geriatrics and
Gerontology, Japan
| |
Collapse
|
|
5
|
Navita, Mittal P, Sharma YK, Rai AK, Simaiya S, Lilhore UK, Kumar V. Gait-based Parkinson's disease diagnosis and severity classification using force sensors and machine learning. Sci Rep 2025; 15:328. [PMID: 39747956 PMCID: PMC11696931 DOI: 10.1038/s41598-024-83357-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2024] [Accepted: 12/13/2024] [Indexed: 01/04/2025] Open
Abstract
A dual-stage model for classifying Parkinson's disease severity, through a detailed analysis of Gait signals using force sensors and machine learning approaches, is proposed in this study. Parkinson's disease is the primary neurodegenerative disorder that results in a gradual reduction in motor function. Early detection and monitoring of the disease progression is highly challenging due to the gradual progression of symptoms and the inadequacy of conventional methods in identifying subtle changes in mobility. The proposed dual-stage model utilized a hypertuned Random Forest Tree (RFT) to classify the subjects into PD and non-PD classes at Stage 1 and a hypertuned Ensemble Regressor (ER) to predict the severity of illness at Stage 2. Further, we have implemented the proposed model on the data signals gathered from both feet of 166 participants using Vertical Ground Reaction Force Sensors (VGRF). The dataset comprised 93 persons with Parkinson's disease and 73 healthy controls. The dataset (imbalance) collected from both feet is passed to the preprocessing phase (for balancing data using the SMOTE method), followed by the feature extraction phase to extract features related to time, frequency, spatial, and temporal features domains that are highly effective for detecting and assigning severity levels of PD. A Recursive Feature Elimination method is also used to select the optimal set of features to improve the model performance. It is acknowledged that the early detection of Parkinson's disease is contingent upon critical parameters, including stride length, stance duration, swing interval, double limb support, step time, and step length. The crucial evaluation metrics used for evaluating model performance include accuracy, mean absolute error, and root mean square error. The findings indicate that the suggested model significantly surpasses current methodologies. It attained an accuracy of 97.5 ± 2.1%, Sensitivity of 97% ± 2.5%, and average Specificity of 95% ± 2.2% in differentiating between PD and non-PD participants utilizing RFT and evaluated disease severity with an average accuracy of 96.4 ± 2.3%, an average mean absolute error of 0.065 ± 0.024, and a root mean square error of 0.080 ± 0.06. The results indicate that the proposed dual-stage model is exceptionally successful in the early detection and severity assessment of Parkinson's disease and demonstrates better efficacy than alternative models.
Collapse
Affiliation(s)
- Navita
- Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, Haryana, India
| | - Pooja Mittal
- Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, Haryana, India
| | - Yogesh Kumar Sharma
- Department of Computer Science & Engineering, KoneruLakshmaiah Education Foundation, Green Field, Vaddeswaram, Guntur, Andhra Pradesh, India
| | - Anjani Kumar Rai
- Department of CEA, GLA University, Mathura, 281406, Uttar Pradesh, India
| | - Sarita Simaiya
- Department of Computer Science & Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India.
- Arba Minch University, Arba Minch, Ethiopia.
| | - Umesh Kumar Lilhore
- Department of Computer Science & Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India.
| | - Vimal Kumar
- Department of Computer Science & Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India
| |
Collapse
|
|
6
|
Barbieri RA, Barbieri FA, Zelada-Astudillo N, Moreno VC, Kalva-Filho CA, Zamunér AR. Influence of Aerobic Exercise on Functional Capacity and Maximal Oxygen Uptake in Patients With Parkinson Disease: A Systematic Review and Meta-analysis. Arch Phys Med Rehabil 2025; 106:134-144. [PMID: 39374688 DOI: 10.1016/j.apmr.2024.09.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 09/11/2024] [Accepted: 09/14/2024] [Indexed: 10/09/2024]
Abstract
OBJECTIVE To determine the effects of aerobic training in randomized controlled clinical trials on functional capacity, motor symptoms, and oxygen consumption in individuals with Parkinson disease (PD) through a systematic literature review and meta-analysis. DATA SOURCES PUBMED, Web of Science, CINAHL, SciELO, and Medline databases were searched to identify published studies until September 2023. STUDY SELECTION Randomized controlled clinical trials that evaluated the long-term effect of aerobic exercise in individuals with PD were included. DATA EXTRACTION Two independent reviewers extracted the data and assessed the risk of bias and the Grading of Recommendation Assessment, Development, and Evaluation. In case of disagreement, a third reviewer was consulted. DATA SYNTHESIS Thirteen studies were included in the systematic review, and the number of participants was 588 with an average age of 66.2 years (57-73y). The study's exercise intervention lasted between 6 and 70 weeks, with most studies lasting 10-12 weeks, with 3 sessions per week and an average duration of 47 minutes per session. The meta-analysis revealed that aerobic exercise is effective in enhancing maximal oxygen uptake (standardized mean difference, SMD 0.42 [95% CI, 0.18, 0.66; P=.0007]) and functional capacity (SMD 0.48 [95% CI, 0.24-0.71; P<.0001]). In addition, aerobic exercise can reduce the motor-unified Parkinson disease rating scale (mean difference-2.48 [95% CI, -3.16 to -1.81; P<.00001]) score in individuals with PD. CONCLUSIONS Aerobic exercise training conducted 2-3 times a week, with different intensities (low to high), can be an effective intervention for enhancing functional capacity, maximizing oxygen uptake, and reducing the UPDRS scores in individuals with PD.
Collapse
Affiliation(s)
| | - Fabio Augusto Barbieri
- Department of Physical Education, Human Movement Research Laboratory (MOVI-LAB), School of Sciences, São Paulo State University (UNESP), Bauru, Brazil
| | - Nicolle Zelada-Astudillo
- Department of Kinesiology, Laboratorio de Investigación Clínica en Kinesiología, Universidad Católica del Maule, Talca, Chile
| | - Vinicius Christianini Moreno
- Department of Physical Education, Human Movement Research Laboratory (MOVI-LAB), School of Sciences, São Paulo State University (UNESP), Bauru, Brazil
| | - Carlos Augusto Kalva-Filho
- Department of Physical Education, Human Movement Research Laboratory (MOVI-LAB), School of Sciences, São Paulo State University (UNESP), Bauru, Brazil
| | - Antonio Roberto Zamunér
- Department of Kinesiology, Laboratorio de Investigación Clínica en Kinesiología, Universidad Católica del Maule, Talca, Chile; Centro de Investigación en Neuropsicología y Neurociencias Cognitivas (CINPSI Neurocog), Universidad Católica del Maule, Talca, Chile.
| |
Collapse
|
|
7
|
Hiromura K, Kitajima H, Hatakenaka C, Shimizu Y, Miyagaki T, Mori M, Nakashima K, Fuku A, Hirata H, Tachi Y, Kaneuji A. Short-Term Effects of Cooled Radiofrequency Ablation on Walking Ability in Japanese Patients with Knee Osteoarthritis. J Clin Med 2024; 13:7049. [PMID: 39685518 DOI: 10.3390/jcm13237049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2024] [Revised: 11/19/2024] [Accepted: 11/20/2024] [Indexed: 12/18/2024] Open
Abstract
Background/Objectives: Knee osteoarthritis (KOA) is a degenerative joint disease typically managed with conservative treatments, such as anti-inflammatory medications and intra-articular hyaluronic acid injections; however, advanced cases may eventually require surgical intervention. Recently, cooled radiofrequency ablation (CRFA) has emerged as a novel treatment option for alleviating KOA-related pain by temporarily disabling pain-transmitting nerves. This study evaluated the short-term effects of CRFA on pain relief and walking ability in KOA patients, with a specific focus on functional improvements in walking capacity. Methods: This study included 58 patients (71 knees) with KOA who underwent CRFA after experiencing inadequate pain control with conservative treatments. The cohort consisted of 28 men and 30 women, with a mean age of 75.2 years (55-90). Under ultrasound guidance, CRFA was performed on the superior lateral geniculate nerve, superior medial geniculate nerve, and inferior medial geniculate nerve, with each targeted nerve ablated. Pre- and post-procedural evaluations (one month after CRFA) included assessments of visual analog scale (VAS) scores for pain at rest and during walking, range of motion (ROM), knee extensor strength, walking speed, and gait stability. Results: Significant improvements in the mean VAS (rest/walking) and mean walking speed (comfortable/maximum) were observed following CRFA. However, no significant changes were noted in ROM, knee extensor strength, or walking stability. Conclusions: These findings suggest that rehabilitation may be essential to further enhance walking stability. Overall, CRFA appears to be a promising short-term treatment option for reducing VAS pain scores and enhancing walking speed in patients with KOA.
Collapse
Affiliation(s)
- Kentaro Hiromura
- Department of Orthopedic Surgery, Kanazawa Medical University, Kahoku 920-0293, Japan
- Department of Orthopedic Surgery, Kanazawa Medical University Himi Municipal Hospital, Himi 935-8531, Japan
| | - Hironori Kitajima
- Department of Orthopedic Surgery, Kanazawa Medical University, Kahoku 920-0293, Japan
- Department of Orthopedic Surgery, Kanazawa Medical University Himi Municipal Hospital, Himi 935-8531, Japan
| | - Chie Hatakenaka
- Department of Orthopedic Surgery, Kanazawa Medical University, Kahoku 920-0293, Japan
- Department of Orthopedic Surgery, Kanazawa Medical University Himi Municipal Hospital, Himi 935-8531, Japan
| | - Yoshiaki Shimizu
- Department of Orthopedic Surgery, Kanazawa Medical University, Kahoku 920-0293, Japan
- Department of Orthopedic Surgery, Kanazawa Medical University Himi Municipal Hospital, Himi 935-8531, Japan
| | - Terumasa Miyagaki
- Department of Rehabilitation, Kanazawa Medical University Himi Municipal Hospital, Himi 935-8531, Japan
| | - Masayuki Mori
- Department of Rehabilitation, Kanazawa Medical University Himi Municipal Hospital, Himi 935-8531, Japan
| | - Kazuhei Nakashima
- Department of Rehabilitation, Kanazawa Medical University Himi Municipal Hospital, Himi 935-8531, Japan
| | - Atsushi Fuku
- Department of Orthopedic Surgery, Kanazawa Medical University, Kahoku 920-0293, Japan
| | - Hiroaki Hirata
- Department of Orthopedic Surgery, Kanazawa Medical University, Kahoku 920-0293, Japan
| | - Yoshiyuki Tachi
- Department of Orthopedic Surgery, Kanazawa Medical University, Kahoku 920-0293, Japan
| | - Ayumi Kaneuji
- Department of Orthopedic Surgery, Kanazawa Medical University, Kahoku 920-0293, Japan
| |
Collapse
|
|
8
|
Lin YP, Lin II, Chiou WD, Chang HC, Chen RS, Lu CS, Chan HL, Chang YJ. Optimizing rehabilitation strategies in Parkinson's disease: a comparison of dual cognitive-walking treadmill training and single treadmill training. Sci Rep 2024; 14:25210. [PMID: 39448695 PMCID: PMC11502839 DOI: 10.1038/s41598-024-75422-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Accepted: 10/04/2024] [Indexed: 10/26/2024] Open
Abstract
Dual cognitive-walking treadmill training (DTT), designed to replicate real-life walking conditions, has shown promise effect in individuals with Parkinson's disease (PD). This study aims to compare the effects of DTT versus single treadmill training (STT) on cognitive and walking performance under both single and dual task conditions, as well as on fall, patients' subjective feeling, and quality of life. Sixteen individuals with PD were randomly assigned to DTT or STT group and underwent 8 weeks of training. The DTT group received treadmill training with cognitive loads, while the STT group received treadmill training without cognitive load. Outcome measures included gait parameters (speed, step length) and cognitive performance (reaction time, accuracy, composite score) under both single and dual task conditions. Unified Parkinson's Disease Rating Scale-part III (UPDRS-III), Falls Efficacy Scale (FES), Patient Global Impression of Change (PGIC), and Parkinson's Disease Questionnaire (PDQ-39) were also measured. Both DTT and STT groups showed increased comfortable walking speed and step length. Only the DTT group demonstrated significant improvements in cognitive composite score under both single and dual task conditions, as well as UPDRS-III, FES, and PDQ-39(p < 0.05). DTT can enhance cognitive function without compromising walking ability and also have real-world transferability.
Collapse
Affiliation(s)
- Yen-Po Lin
- Department of Medical Education, Chang Gung Memorial Hospital Keelung, Keelung, Taiwan
| | - I-I Lin
- School of Physical Therapy and Graduate Institute of Rehabilitation Science, College of Medicine, Chang Gung University, 259, Wen-Hwa 1st Rd, Kweishan, Taoyuan, Taiwan
| | - Wei-Da Chiou
- School of Physical Therapy and Graduate Institute of Rehabilitation Science, College of Medicine, Chang Gung University, 259, Wen-Hwa 1st Rd, Kweishan, Taoyuan, Taiwan
- Department of Physical Rehabilitation, Kaohsiung Armed Forces General Hospital, Kaohsiung, Taiwan
| | | | - Rou-Shayn Chen
- Department of Neurology, Chang Gung Memorial Hospital Linkou, Taoyuan, Taiwan
- School of Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Neuroscience Research Center, Chang Gung Memorial Hospital Linkou, Taoyuan, Taiwan
| | - Chin-Song Lu
- Professor Lu Neurological Clinic, Taoyuan, Taiwan
| | - Hsiao-Lung Chan
- Neuroscience Research Center, Chang Gung Memorial Hospital Linkou, Taoyuan, Taiwan
- Department of Electrical Engineering, Department of Biomedical Engineering, Chang Gung University, Taoyuan, Taiwan
| | - Ya-Ju Chang
- School of Physical Therapy and Graduate Institute of Rehabilitation Science, College of Medicine, Chang Gung University, 259, Wen-Hwa 1st Rd, Kweishan, Taoyuan, Taiwan.
- Neuroscience Research Center, Chang Gung Memorial Hospital Linkou, Taoyuan, Taiwan.
- Healthy Aging Research Center, Chang Gung University, Taoyuan, Taiwan.
| |
Collapse
|
|
9
|
Min YS, Jung TD, Lee YS, Kwon Y, Kim HJ, Kim HC, Lee JC, Park E. Biomechanical Gait Analysis Using a Smartphone-Based Motion Capture System (OpenCap) in Patients with Neurological Disorders. Bioengineering (Basel) 2024; 11:911. [PMID: 39329653 PMCID: PMC11429388 DOI: 10.3390/bioengineering11090911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2024] [Revised: 09/09/2024] [Accepted: 09/09/2024] [Indexed: 09/28/2024] Open
Abstract
This study evaluates the utility of OpenCap (v0.3), a smartphone-based motion capture system, for performing gait analysis in patients with neurological disorders. We compared kinematic and kinetic gait parameters between 10 healthy controls and 10 patients with neurological conditions, including stroke, Parkinson's disease, and cerebral palsy. OpenCap captured 3D movement dynamics using two smartphones, with data processed through musculoskeletal modeling. The key findings indicate that the patient group exhibited significantly slower gait speeds (0.67 m/s vs. 1.10 m/s, p = 0.002), shorter stride lengths (0.81 m vs. 1.29 m, p = 0.001), and greater step length asymmetry (107.43% vs. 91.23%, p = 0.023) compared to the controls. Joint kinematic analysis revealed increased variability in pelvic tilt, hip flexion, knee extension, and ankle dorsiflexion throughout the gait cycle in patients, indicating impaired motor control and compensatory strategies. These results indicate that OpenCap can effectively identify significant gait differences, which may serve as valuable biomarkers for neurological disorders, thereby enhancing its utility in clinical settings where traditional motion capture systems are impractical. OpenCap has the potential to improve access to biomechanical assessments, thereby enabling better monitoring of gait abnormalities and informing therapeutic interventions for individuals with neurological disorders.
Collapse
Affiliation(s)
- Yu-Sun Min
- Department of Rehabilitation Medicine, School of Medicine, Kyungpook National University, Daegu 41944, Republic of Korea; (Y.-S.M.); (T.-D.J.); (Y.-S.L.)
- Department of Rehabilitation Medicine, Kyungpook National University Chilgok Hospital, Daegu 41404, Republic of Korea;
- AI-Driven Convergence Software Education Research Program, Graduate School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
- Department of Biomedical Engineering, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea; (H.C.K.); (J.C.L.)
| | - Tae-Du Jung
- Department of Rehabilitation Medicine, School of Medicine, Kyungpook National University, Daegu 41944, Republic of Korea; (Y.-S.M.); (T.-D.J.); (Y.-S.L.)
- Department of Rehabilitation Medicine, Kyungpook National University Chilgok Hospital, Daegu 41404, Republic of Korea;
| | - Yang-Soo Lee
- Department of Rehabilitation Medicine, School of Medicine, Kyungpook National University, Daegu 41944, Republic of Korea; (Y.-S.M.); (T.-D.J.); (Y.-S.L.)
- Department of Rehabilitation Medicine, Kyungpook National University Hospital, Daegu 41944, Republic of Korea;
| | - Yonghan Kwon
- Department of Rehabilitation Medicine, Kyungpook National University Hospital, Daegu 41944, Republic of Korea;
| | - Hyung Joon Kim
- Department of Rehabilitation Medicine, Kyungpook National University Chilgok Hospital, Daegu 41404, Republic of Korea;
| | - Hee Chan Kim
- Department of Biomedical Engineering, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea; (H.C.K.); (J.C.L.)
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul 08826, Republic of Korea
| | - Jung Chan Lee
- Department of Biomedical Engineering, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea; (H.C.K.); (J.C.L.)
- Institute of Bioengineering, Seoul National University, Seoul 03080, Republic of Korea
- Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul 03080, Republic of Korea
| | - Eunhee Park
- Department of Rehabilitation Medicine, School of Medicine, Kyungpook National University, Daegu 41944, Republic of Korea; (Y.-S.M.); (T.-D.J.); (Y.-S.L.)
- Department of Rehabilitation Medicine, Kyungpook National University Chilgok Hospital, Daegu 41404, Republic of Korea;
- AI-Driven Convergence Software Education Research Program, Graduate School of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
| |
Collapse
|
|
10
|
Alharthi AS. Interpretable machine learning comprehensive human gait deterioration analysis. Front Neuroinform 2024; 18:1451529. [PMID: 39247901 PMCID: PMC11377268 DOI: 10.3389/fninf.2024.1451529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Accepted: 07/29/2024] [Indexed: 09/10/2024] Open
Abstract
Introduction Gait analysis, an expanding research area, employs non-invasive sensors and machine learning techniques for a range of applications. In this study, we investigate the impact of cognitive decline conditions on gait performance, drawing connections between gait deterioration in Parkinson's Disease (PD) and healthy individuals dual tasking. Methods We employ Explainable Artificial Intelligence (XAI) specifically Layer-Wise Relevance Propagation (LRP), in conjunction with Convolutional Neural Networks (CNN) to interpret the intricate patterns in gait dynamics influenced by cognitive loads. Results We achieved classification accuracies of 98% F1 scores for PD dataset and 95.5% F1 scores for the combined PD dataset. Furthermore, we explore the significance of cognitive load in healthy gait analysis, resulting in robust classification accuracies of 90% ± 10% F1 scores for subject cognitive load verification. Our findings reveal significant alterations in gait parameters under cognitive decline conditions, highlighting the distinctive patterns associated with PD-related gait impairment and those induced by multitasking in healthy subjects. Through advanced XAI techniques (LRP), we decipher the underlying features contributing to gait changes, providing insights into specific aspects affected by cognitive decline. Discussion Our study establishes a novel perspective on gait analysis, demonstrating the applicability of XAI in elucidating the shared characteristics of gait disturbances in PD and dual-task scenarios in healthy individuals. The interpretability offered by XAI enhances our ability to discern subtle variations in gait patterns, contributing to a more nuanced comprehension of the factors influencing gait dynamics in PD and dual-task conditions, emphasizing the role of XAI in unraveling the intricacies of gait control.
Collapse
Affiliation(s)
- Abdullah S Alharthi
- Department of Electrical Engineering, College of Engineering King Khalid University, Abha, Saudi Arabia
| |
Collapse
|
|
11
|
Fortunati M, Febbi M, Negro M, Gennaro F, D’Antona G, Crisafulli O. Lower-Limb Exoskeletons for Gait Training in Parkinson's Disease: The State of the Art and Future Perspectives. Healthcare (Basel) 2024; 12:1636. [PMID: 39201194 PMCID: PMC11353983 DOI: 10.3390/healthcare12161636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2024] [Revised: 08/07/2024] [Accepted: 08/14/2024] [Indexed: 09/02/2024] Open
Abstract
Gait dysfunction (GD) is a common impairment of Parkinson's disease (PD), which negatively impacts patients' quality of life. Among the most recent rehabilitation technologies, a lower-limb powered exoskeleton (LLEXO) arises as a useful instrument for gait training in several neurological conditions, including PD. However, some questions relating to methods of use, achievable results, and usefulness compared to traditional rehabilitation methodologies still require clear answers. Therefore, in this review, we aim to summarise and analyse all the studies that have applied an LLEXO to train gait in PD patients. Literature research on PubMed and Scopus retrieved five articles, comprising 46 PD participants stable on medications (age: 71.7 ± 3.7 years, 24 males, Hoehn and Yahr: 2.1 ± 0.6). Compared to traditional rehabilitation, low-profile lower-limb exoskeleton (lp-LLEXO) training brought major improvements towards walking capacity and gait speed, while there are no clear major benefits regarding the dual-task gait cost index and freezing of gait symptoms. Importantly, the results suggest that lp-LLEXO training is more beneficial for patients with an intermediate-to-severe level of disease severity (Hoehn and Yahr > 2.5). This review could provide a novel framework for implementing LLEXO in clinical practise, highlighting its benefits and limitations towards gait training.
Collapse
Affiliation(s)
- Matteo Fortunati
- Department of Industrial Engineering, University of Tor Vergata, 00133 Rome, Italy
- CRIAMS-Sport Medicine Centre Voghera, University of Pavia, 27058 Voghera, Italy
| | - Massimiliano Febbi
- Department of Industrial Engineering, University of Tor Vergata, 00133 Rome, Italy
- Laboratory for Rehabilitation, Medicine and Sport (LARM), 00133 Rome, Italy
| | - Massimo Negro
- CRIAMS-Sport Medicine Centre Voghera, University of Pavia, 27058 Voghera, Italy
| | - Federico Gennaro
- Department of Biomedical Sciences, University of Padua, 35131 Padua, Italy
| | - Giuseppe D’Antona
- CRIAMS-Sport Medicine Centre Voghera, University of Pavia, 27058 Voghera, Italy
- Department of Public Health, Experimental and Forensic Medicine, University of Pavia, 27100 Pavia, Italy
| | - Oscar Crisafulli
- CRIAMS-Sport Medicine Centre Voghera, University of Pavia, 27058 Voghera, Italy
| |
Collapse
|
|
12
|
Dong B, Brückerhoff-Plückelmann F, Meyer L, Dijkstra J, Bente I, Wendland D, Varri A, Aggarwal S, Farmakidis N, Wang M, Yang G, Lee JS, He Y, Gooskens E, Kwong DL, Bienstman P, Pernice WHP, Bhaskaran H. Partial coherence enhances parallelized photonic computing. Nature 2024; 632:55-62. [PMID: 39085539 PMCID: PMC11291273 DOI: 10.1038/s41586-024-07590-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 05/17/2024] [Indexed: 08/02/2024]
Abstract
Advancements in optical coherence control1-5 have unlocked many cutting-edge applications, including long-haul communication, light detection and ranging (LiDAR) and optical coherence tomography6-8. Prevailing wisdom suggests that using more coherent light sources leads to enhanced system performance and device functionalities9-11. Our study introduces a photonic convolutional processing system that takes advantage of partially coherent light to boost computing parallelism without substantially sacrificing accuracy, potentially enabling larger-size photonic tensor cores. The reduction of the degree of coherence optimizes bandwidth use in the photonic convolutional processing system. This breakthrough challenges the traditional belief that coherence is essential or even advantageous in integrated photonic accelerators, thereby enabling the use of light sources with less rigorous feedback control and thermal-management requirements for high-throughput photonic computing. Here we demonstrate such a system in two photonic platforms for computing applications: a photonic tensor core using phase-change-material photonic memories that delivers parallel convolution operations to classify the gaits of ten patients with Parkinson's disease with 92.2% accuracy (92.7% theoretically) and a silicon photonic tensor core with embedded electro-absorption modulators (EAMs) to facilitate 0.108 tera operations per second (TOPS) convolutional processing for classifying the Modified National Institute of Standards and Technology (MNIST) handwritten digits dataset with 92.4% accuracy (95.0% theoretically).
Collapse
Affiliation(s)
- Bowei Dong
- Department of Materials, University of Oxford, Oxford, UK
- Institute of Microelectronics, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | | | - Lennart Meyer
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
| | - Jelle Dijkstra
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
| | - Ivonne Bente
- Center for NanoTechnology, University of Münster, Münster, Germany
| | - Daniel Wendland
- Center for NanoTechnology, University of Münster, Münster, Germany
| | - Akhil Varri
- Center for NanoTechnology, University of Münster, Münster, Germany
| | | | | | - Mengyun Wang
- Department of Materials, University of Oxford, Oxford, UK
| | - Guoce Yang
- Department of Materials, University of Oxford, Oxford, UK
| | - June Sang Lee
- Department of Materials, University of Oxford, Oxford, UK
| | - Yuhan He
- Department of Materials, University of Oxford, Oxford, UK
| | | | - Dim-Lee Kwong
- Institute of Microelectronics, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Peter Bienstman
- Photonics Research Group, Ghent University - imec, Ghent, Belgium
| | - Wolfram H P Pernice
- Kirchhoff-Institute for Physics, Heidelberg University, Heidelberg, Germany
- Center for NanoTechnology, University of Münster, Münster, Germany
| | | |
Collapse
|
|
13
|
Di Bacco VE, Gage WH. Gait variability, fractal dynamics, and statistical regularity of treadmill and overground walking recorded with a smartphone. Gait Posture 2024; 111:53-58. [PMID: 38636334 DOI: 10.1016/j.gaitpost.2024.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 03/20/2024] [Accepted: 04/04/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND The nonlinear variability present during steady-state gait may provide a signature of health and showcase one's walking adaptability. Although treadmills can capture vast amounts of walking data required for estimating variability within a small space, gait patterns may be misrepresented compared to an overground setting. Smartphones may provide a low-cost and user-friendly estimate of gait patterns among a variety of walking settings. However, no study has investigated differences in gait patterns derived from a smartphone between treadmill walking (TW) and overground walking (OW). RESEARCH QUESTION This study implemented a smartphone accelerometer to compare differences in temporal gait variability and gait dynamics between TW and OW. METHODS Sixteen healthy adults (8F; 24.7 ± 3.8 years) visited the laboratory on three separate days and completed three 8-minute OW and three TW trials, at their preferred speed, during each visit. The inter-stride interval was calculated as the time difference between right heel contact events located within the vertical accelerometery signals recorded from a smartphone while placed in participants front right pant pocket during walking trials. The inter-stride interval series was used to calculate stride time standard deviation (SD) and coefficient of variation (COV), statistical persistence (fractal scaling index), and statistical regularity (sample entropy). Two-way analysis of variance compared walking condition and laboratory visits for each measure. RESULTS Compared to TW, OW displayed significantly (p < 0.01) greater stride time SD (0.014 s, 0.020 s), COV (1.26 %, 1.82 %), fractal scaling index (0.70, 0.79) and sample entropy (1.43, 1.63). No differences were found between visits for all measures. SIGNIFICANCE Smartphone-based assessment of gait provides the ability to distinguish between OW and TW conditions, similar to previously established methodologies. Furthermore, smartphones may be a low-cost and user-friendly tool to estimate gait patterns outside the laboratory to improve ecological validity, with implications for free-living monitoring of gait among various populations.
Collapse
Affiliation(s)
- Vincenzo E Di Bacco
- School of Kinesiology and Health Science, York University, Toronto, Ontario, Canada.
| | - William H Gage
- School of Kinesiology and Health Science, York University, Toronto, Ontario, Canada
| |
Collapse
|
|
14
|
Jovanovic L, Damaševičius R, Matic R, Kabiljo M, Simic V, Kunjadic G, Antonijevic M, Zivkovic M, Bacanin N. Detecting Parkinson's disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics. PeerJ Comput Sci 2024; 10:e2031. [PMID: 38855236 PMCID: PMC11157549 DOI: 10.7717/peerj-cs.2031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 04/09/2024] [Indexed: 06/11/2024]
Abstract
Neurodegenerative conditions significantly impact patient quality of life. Many conditions do not have a cure, but with appropriate and timely treatment the advance of the disease could be diminished. However, many patients only seek a diagnosis once the condition progresses to a point at which the quality of life is significantly impacted. Effective non-invasive and readily accessible methods for early diagnosis can considerably enhance the quality of life of patients affected by neurodegenerative conditions. This work explores the potential of convolutional neural networks (CNNs) for patient gain freezing associated with Parkinson's disease. Sensor data collected from wearable gyroscopes located at the sole of the patient's shoe record walking patterns. These patterns are further analyzed using convolutional networks to accurately detect abnormal walking patterns. The suggested method is assessed on a public real-world dataset collected from parents affected by Parkinson's as well as individuals from a control group. To improve the accuracy of the classification, an altered variant of the recent crayfish optimization algorithm is introduced and compared to contemporary optimization metaheuristics. Our findings reveal that the modified algorithm (MSCHO) significantly outperforms other methods in accuracy, demonstrated by low error rates and high Cohen's Kappa, precision, sensitivity, and F1-measures across three datasets. These results suggest the potential of CNNs, combined with advanced optimization techniques, for early, non-invasive diagnosis of neurodegenerative conditions, offering a path to improve patient quality of life.
Collapse
Affiliation(s)
- Luka Jovanovic
- Faculty of Technical Sciences, Singidunum University, Belgrade, Serbia
| | | | - Rade Matic
- Department for Information Systems and Technologies, Belgrade Academy for Business and Arts Applied Studies, Belgrade, Serbia
| | - Milos Kabiljo
- Department for Information Systems and Technologies, Belgrade Academy for Business and Arts Applied Studies, Belgrade, Serbia
| | - Vladimir Simic
- Faculty of Transport and Traffic Engineering, University of Belgrade, Belgrade, Serbia
- College of Engineering, Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan City, Taiwan
| | - Goran Kunjadic
- Higher Colleges of Technology, Abu Dhabi, United Arab Emirates
| | - Milos Antonijevic
- Faculty of Informatics and Computing, Singidunum University, Belgrade, Serbia
| | - Miodrag Zivkovic
- Faculty of Informatics and Computing, Singidunum University, Belgrade, Serbia
| | - Nebojsa Bacanin
- Faculty of Informatics and Computing, Singidunum University, Belgrade, Serbia
- MEU Research Unit, Middle East University, Amman, Jordan
| |
Collapse
|
|
15
|
Baudendistel ST, Franz JR, Schmitt AC, Wade FE, Pappas MC, Au KLK, Hass CJ. Visual feedback improves propulsive force generation during treadmill walking in people with Parkinson disease. J Biomech 2024; 167:112073. [PMID: 38599018 PMCID: PMC11046741 DOI: 10.1016/j.jbiomech.2024.112073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 01/29/2024] [Accepted: 04/02/2024] [Indexed: 04/12/2024]
Abstract
Persons with Parkinson's disease experience gait alterations, such as reduced step length. Gait dysfunction is a significant research priority as the current treatments targeting gait impairment are limited. This study aimed to investigate the effects of visual biofeedback on propulsive force during treadmill walking in persons with Parkinson's. Sixteen ambulatory persons with Parkinson's participated in the study. They received real-time biofeedback of anterior ground reaction force during treadmill walking at a constant speed. Peak propulsive force values were measured and normalized to body weight. Spatiotemporal parameters were also assessed, including stride length and double support percent. Persons with Parkinson's significantly increased peak propulsive force during biofeedback compared to baseline (p <.0001, Cohen's dz = 1.69). Variability in peak anterior ground reaction force decreased across repeated trials (p <.0001, dz = 1.51). While spatiotemporal parameters did not show significant changes individually, stride length and double support percent improved marginally during biofeedback trials. Persons with Parkinson's can increase propulsive force with visual biofeedback, suggesting the presence of a propulsive reserve. Though stride length did not significantly change, clinically meaningful improvements were observed. Targeting push-off force through visual biofeedback may offer a potential rehabilitation technique to enhance gait performance in Persons with Parkinson's. Future studies could explore the long-term efficacy of this intervention and investigate additional strategies to improve gait in Parkinson's disease.
Collapse
Affiliation(s)
- Sidney T Baudendistel
- Program in Physical Therapy, Washington University in St. Louis School of Medicine, St. Louis, MO, USA; Department of Applied Physiology & Kinesiology, University of Florida, Gainesville, FL, USA.
| | - Jason R Franz
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, USA
| | - Abigail C Schmitt
- Department of Health, Human Performance, and Recreation, University of Arkansas, Fayetteville, AR, USA
| | - Francesca E Wade
- School of Exercise and Nutritional Sciences, San Diego State University, San Diego, CA, USA
| | - Marc C Pappas
- Department of Applied Physiology & Kinesiology, University of Florida, Gainesville, FL, USA
| | | | - Chris J Hass
- Department of Applied Physiology & Kinesiology, University of Florida, Gainesville, FL, USA; Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA
| |
Collapse
|
|
16
|
Daadi EW, Daadi ES, Oh T, Li M, Kim J, Daadi MM. Combining physical & cognitive training with iPSC-derived dopaminergic neuron transplantation promotes graft integration & better functional outcome in parkinsonian marmosets. Exp Neurol 2024; 374:114694. [PMID: 38272159 DOI: 10.1016/j.expneurol.2024.114694] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 01/09/2024] [Accepted: 01/15/2024] [Indexed: 01/27/2024]
Abstract
Parkinson's disease (PD) is a relentlessly progressive and currently incurable neurodegenerative disease with significant unmet medical needs. Since PD stems from the degeneration of midbrain dopaminergic (DA) neurons in a defined brain location, PD patients are considered optimal candidates for cell replacement therapy. Clinical trials for cell transplantation in PD are beginning to re-emerge worldwide with a new focus on induced pluripotent stem cells (iPSCs) as a source of DA neurons since they can be derived from adult somatic cells and produced in large quantities under current good manufacturing practices. However, for this therapeutic strategy to be realized as a viable clinical option, fundamental translational challenges need to be addressed including the manufacturing process, purity and efficacy of the cells, the method of delivery, the extent of host reinnervation and the impact of patient-centered adjunctive interventions. In this study we report on the impact of physical and cognitive training (PCT) on functional recovery in the nonhuman primate (NHP) model of PD after cell transplantation. We observed that at 6 months post-transplant, the PCT group returned to normal baseline in their daily activity measured by actigraphy, significantly improved in their sensorimotor and cognitive tasks, and showed enhanced synapse formation between grafted cells and host cells. We also describe a robust, simple, efficient, scalable, and cost-effective manufacturing process of engraftable DA neurons derived from iPSCs. This study suggests that integrating PCT with cell transplantation therapy could promote optimal graft functional integration and better outcome for patients with PD.
Collapse
Affiliation(s)
- Etienne W Daadi
- Southwest National Primate Research Center, Texas Biomedical Research Institute, 8715 W. Military Drive, San Antonio, TX 78227, USA
| | - Elyas S Daadi
- Southwest National Primate Research Center, Texas Biomedical Research Institute, 8715 W. Military Drive, San Antonio, TX 78227, USA
| | - Thomas Oh
- Southwest National Primate Research Center, Texas Biomedical Research Institute, 8715 W. Military Drive, San Antonio, TX 78227, USA
| | - Mingfeng Li
- Department of Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA
| | - Jeffrey Kim
- Southwest National Primate Research Center, Texas Biomedical Research Institute, 8715 W. Military Drive, San Antonio, TX 78227, USA; Department of Cell Systems & Anatomy, Long School of Medicine, University of Texas Health at San Antonio, 7703 Floyd Curl Dr., San Antonio, TX 78229, USA
| | - Marcel M Daadi
- Southwest National Primate Research Center, Texas Biomedical Research Institute, 8715 W. Military Drive, San Antonio, TX 78227, USA; Department of Cell Systems & Anatomy, Long School of Medicine, University of Texas Health at San Antonio, 7703 Floyd Curl Dr., San Antonio, TX 78229, USA; Department of Radiology, Long School of Medicine, University of Texas Health at San Antonio, 7703 Floyd Curl Dr., San Antonio, TX 78229, USA.
| |
Collapse
|
|
17
|
Cerasa A. Fractals in Neuropsychology and Cognitive Neuroscience. ADVANCES IN NEUROBIOLOGY 2024; 36:761-778. [PMID: 38468062 DOI: 10.1007/978-3-031-47606-8_38] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
The fractal dimension of cognition refers to the idea that the cognitive processes of the human brain exhibit fractal properties. This means that certain patterns of cognitive activity, such as visual perception, memory, language, or problem-solving, can be described using the mathematical concept of fractal dimension.The idea that cognition is fractal has been proposed by some researchers as a way to understand the complex, self-similar nature of the human brain. However, it's a relatively new idea and is still under investigation, so it's not yet clear to what extent cognitive processes exhibit fractal properties or what implications this might have for our understanding of the brain and clinical practice. Indeed, the mission of the "fractal neuroscience" field is to define the characteristics of fractality in human cognition in order to differently characterize the emergence of brain disorders.
Collapse
Affiliation(s)
- Antonio Cerasa
- Institute for Biomedical Research and Innovation, National Research Council, IRIB-CNR, Messina, Italy
- S. Anna Institute, Crotone, Italy
- Pharmacotechnology Documentation and Transfer Unit, Preclinical and Translational Pharmacology, Department of Pharmacy, Health Science and Nutrition, University of Calabria, Arcavacata, Italy
| |
Collapse
|
|
18
|
Nair P, Shojaei Baghini M, Pendharkar G, Chung H. Detecting early-stage Parkinson's disease from gait data. Proc Inst Mech Eng H 2023; 237:1287-1296. [PMID: 37916586 DOI: 10.1177/09544119231197090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2023]
Abstract
Parkinson's disease is a chronic and progressive neurodegenerative disorder with an estimated 10 million people worldwide living with PD. Since early signs are benign, many patients go undiagnosed until the symptoms get severe and the treatment becomes more difficult. The symptoms start intermittently and gradually become continuous as the disease progresses. In order to detect and classify these minute differences between gaits in early PD patients, we propose to use dynamic time warping (DTW). For a given set of gait data from a patient, the DTW algorithm computes the difference between any two gait cycles in the form of a warping path, which reveals small time differences between gait cycles. Once the time-warping information between all possible pairs of gait cycles is used as the main source of gait features, K-means clustering is used to extract the final features. These final features are fed to a simple logistic regression to easily and successfully detect early PD symptoms, which was reported as challenging using conventional statistical features. In addition, the use of DTW ensures that the obtained results are not affected by the differences in the style and speed of walking of a subject. Our approach is validated for the gait data from 83 subjects at early stages of PD, 10 subjects at moderate stages of PD, and 73 controls using the Leave-One-Out and N-fold cross-validation techniques, with a detection accuracy of over 98%. The high classification accuracy validated from a large data set suggests that these new features from DTW can be effectively used to help clinicians diagnose the disease at the earliest. Even though PD is not completely curable, early diagnosis would help clinicians to start the treatment from the beginning thereby reducing the intensity of symptoms at later stages.
Collapse
Affiliation(s)
- Parvathy Nair
- IITB-Monash Research Academy, Mumbai, Maharashtra, India
- IIT Bombay, Mumbai, Maharashtra, India
- Monash University, Clayton, VIC, Australia
| | | | | | - Hoam Chung
- Monash University, Clayton, VIC, Australia
| |
Collapse
|
|
19
|
Fernandez-Del-Olmo M, Sánchez-Molina JA, Novo-Ponte S, Fogelson N. Directed connectivity in Parkinson's disease patients during over-ground and treadmill walking. Exp Gerontol 2023; 178:112220. [PMID: 37230335 DOI: 10.1016/j.exger.2023.112220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 05/17/2023] [Accepted: 05/22/2023] [Indexed: 05/27/2023]
Abstract
Treadmill walking is considered a useful therapeutic tool for improving gait in Parkinson's disease (PD) patients. The study investigated the role of top-down, frontal-parietal versus bottom-up parietal-frontal networks, during over-ground and treadmill walking in PD and control subjects, using functional connectivity. To this end, EEG was recorded simultaneously, during a ten-minute period of continuous walking either over-ground or on a treadmill, in thirteen PD patients and thirteen age-matched controls. We evaluated EEG directed connectivity, using phase transfer entropy in three frequency bands: theta, alpha and beta. PD patients showed increased top-down connectivity during over-ground compared with treadmill walking, in the beta frequency range. Control subjects showed no significant differences in connectivity between the two walking conditions. Our results suggest that in PD patients, OG walking was associated with increased allocation of attentional resources, compared with that on the TL. These functional connectivity modulations may shed further light on the mechanisms underlying treadmill versus overground walking in PD.
Collapse
Affiliation(s)
| | | | - Sabela Novo-Ponte
- Department of Neurology, Hospital Universitario Puerta de Hierro, Majadahonda, Madrid, Spain
| | - Noa Fogelson
- Department of Humanities, University Rey Juan Carlos, Madrid, Spain.
| |
Collapse
|
|
20
|
Chai KXY, Marie Goodwill A, Leuk JSP, Teo WP. Treadmill Walking Maintains Dual-task Gait Performance and Reduces Frontopolar Cortex Activation in Healthy Adults. Neuroscience 2023; 521:148-156. [PMID: 37105393 DOI: 10.1016/j.neuroscience.2023.04.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 02/20/2023] [Accepted: 04/14/2023] [Indexed: 04/29/2023]
Abstract
Studies examining dual-task gait (DTG) have used varying conditions such as overground or treadmill walking, however it is not known whether brain activation patterns differ during these conditions. Therefore, this study compared oxyhaemoglobin (O2Hb) responses of the prefrontal cortex (PFC) during overground and treadmill walking. A total of 30 participants (14M/16F) were recruited in a randomized crossover study comparing overground and treadmill walking under single- and dual-task (STG and DTG) conditions. The DTG consisted of performing walking and cognitive (serial subtraction by 7's) tasks concurrently. A portable 24-channel functional near-infrared spectroscopy system was placed over the PFC, corresponding the left and right dorsolateral PFC and frontopolar cortices (DLPFC and FPC) during overground and treadmill STG and DTG. Results showed a reduction in gait speed during DTG compared to STG on overground but not treadmill walking, while cognitive performance was maintained during DTG on both overground and treadmill walking. A reduction in O2Hb was seen in the FPC during DTG compared to a cognitive task only, and on the treadmill compared to overground walking. Increased activation was seen in the left and right DLPFC during DTG but did not differ between treadmill and overground walking. Our results support the concept of improved gait efficiency during treadmill walking, indicated by the lack of change in STG and DTG performance and concomitant with a reduction in FPC activation. These findings suggest different neural strategies underpinning treadmill and overground walking, which should be considered when designing gait assessment and rehabilitation interventions.
Collapse
Affiliation(s)
- Keller Xin-Yu Chai
- Physical Education and Sports Science Academic Group, National Institute of Education, Nanyang Technological University, Singapore
| | - Alicia Marie Goodwill
- Physical Education and Sports Science Academic Group, National Institute of Education, Nanyang Technological University, Singapore
| | - Jessie Siew-Pin Leuk
- Physical Education and Sports Science Academic Group, National Institute of Education, Nanyang Technological University, Singapore
| | - Wei-Peng Teo
- Physical Education and Sports Science Academic Group, National Institute of Education, Nanyang Technological University, Singapore.
| |
Collapse
|
|
21
|
Stroop in motion: Neurodynamic modulation underlying interference control while sitting, standing, and walking. Biol Psychol 2023; 178:108543. [PMID: 36931590 DOI: 10.1016/j.biopsycho.2023.108543] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 03/10/2023] [Accepted: 03/14/2023] [Indexed: 03/17/2023]
Abstract
There is conflicting evidence about how interference control in healthy adults is affected by walking as compared to standing or sitting. Although the Stroop paradigm is one of the best-studied paradigms to investigate interference control, the neurodynamics associated with the Stroop task during walking have never been studied. We investigated three Stroop tasks using variants with increasing interference levels - word-reading, ink-naming, and the switching of the two tasks, combined in a systematic dual-tasking fashion with three motor conditions - sitting, standing, and treadmill walking. Neurodynamics underlying interference control were recorded using the electroencephalogram. Worsened performance was observed for the incongruent compared to congruent trials and for the switching Stroop compared to the other two variants. The early frontocentral event-related potentials (ERPs) associated with executive functions (P2, N2) differentially signaled posture-related workloads, while the later stages of information processing indexed faster interference suppression and response selection in walking compared to static conditions. The early P2 and N2 components as well as frontocentral Theta and parietal Alpha power were sensitive to increasing workloads on the motor and cognitive systems. The distinction between the type of load (motor and cognitive) became evident only in the later posterior ERP components in which the amplitude non-uniformly reflected the relative attentional demand of a task. Our data suggest that walking might facilitate selective attention and interference control in healthy adults. Existing interpretations of ERP components recorded in stationary settings should be considered with care as they might not be directly transferable to mobile settings.
Collapse
|
|
22
|
Dong C, Chen Y, Huan Z, Li Z, Gao G, Zhou B. An “optical flow” method based on pressure sensors data for quantification of Parkinson's disease characteristics. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
|
23
|
Jungen P, Batista JP, Kirchner M, Habel U, Bollheimer LC, Huppertz C. Variability and symmetry of gait kinematics under dual-task performance of older patients with depression. Aging Clin Exp Res 2023; 35:283-291. [PMID: 36399324 PMCID: PMC9895023 DOI: 10.1007/s40520-022-02295-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 10/24/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND Depression in old age is associated with an increased fall risk. Especially in cognitively challenging situations, fall-promoting gait deviations could appear due to depression- and age-related cognitive deficits. AIM This study investigates (i) whether there are differences in gait performance between depressed older patients and healthy controls and (ii) if gait patterns aggravate when performing a cognitive task whilst walking. METHODS 16 depressed older patients (mean age: 73.1 ± 5.8 years) and 19 healthy controls (mean age: 73.3 ± 6.1 years) were included in the study. Spatiotemporal gait parameters (speed, stride length, swing time) and minimum toe clearance were recorded using a three-dimensional motion-capture system under a single- and a dual-task condition (counting backwards). RESULTS After Bonferroni correction, depressed older patients showed significantly slower walking speed, shorter strides and smaller minimum toe clearance, as well as greater variability in stride length than healthy controls. Under the dual-task, gait performance deteriorated compared with single-task, with slower gait speed, shorter strides, and longer swing time. DISCUSSION Slower walking speed and shorter steps of depressed patients may be a strategy to counteract their fall risk. Increased variability suggests a less stable gait pattern in patients, which could be a reason for their increased fall risk. CONCLUSIONS Depression in old age has a strong effect on gait performance. Possible interventions that might prevent falls in this vulnerable group are discussed. The study was registered at Open Science Framework on May 18, 2021 (publicly accessible May 30, 2023).
Collapse
Affiliation(s)
- Pia Jungen
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen University, Pauwelsstraße 30, 52074 Aachen, Germany
| | - João P. Batista
- Department of Geriatrics, Faculty of Medicine, RWTH Aachen University, Morillenhang 27, 52074 Aachen, Germany ,School of Physical Therapy, Campus Rheinland, SRH University of Applied Health Sciences, 51377 Leverkusen, Germany
| | - Miriam Kirchner
- Alexianer Aachen GmbH, Alexianergraben 33, 52062 Aachen, Germany
| | - Ute Habel
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen University, Pauwelsstraße 30, 52074 Aachen, Germany ,Institute of Neuroscience and Medicine 10, Research Centre Jülich, Wilhelm-Johnen-Straße, 52428 Jülich, Germany
| | - L. Cornelius Bollheimer
- Department of Geriatrics, Faculty of Medicine, RWTH Aachen University, Morillenhang 27, 52074 Aachen, Germany
| | - Charlotte Huppertz
- Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen University, Pauwelsstraße 30, 52074 Aachen, Germany
| |
Collapse
|
|
24
|
Patoz A, Malatesta D, Burtscher J. Isolating the speed factor is crucial in gait analysis for Parkinson's disease. Front Neurosci 2023; 17:1119390. [PMID: 37152600 PMCID: PMC10160620 DOI: 10.3389/fnins.2023.1119390] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 03/31/2023] [Indexed: 05/09/2023] Open
Abstract
Introduction Parkinson's disease (PD) is characterized by an alteration of the walking gait, frequently including a slower self-selected walking speed (SSWS). Although the reduction of walking speed is inherent to people with PD, such speed reduction also represents a potential confounding factor that might partly explain the observed gait differences between PD and control participants. Methods In this study, each participant walked along a 25 m level corridor during which vertical ground reaction force signals were recorded using shoes equipped with eight pressure sensors. Vertical ground reaction force signals (using statistical parametric mapping) and temporal and kinetic variables as well as their related variability and asymmetry (using Student's t-test) were compared between PD (n = 54) and walking-speed-matched control subjects (n = 39). Results Statistical parametric mapping did not yield significant differences between PD and control groups for the vertical ground reaction force signal along the walking stance phase. Stride time and single support time (equivalent to swing time) were shorter and peak vertical ground reaction force was larger in PD patients compared to controls (p ≤ 0.05). However, the single support time was no longer different between people with PD and healthy subjects when expressed relatively to stride time (p = 0.07). While single support, double support, and stance times were significantly more variable and asymmetric for PD than for the control group (p ≤ 0.05), stride time was similar (p ≥ 0.07). Discussion These results indicate that at matched SSWS, PD patients adopt a higher cadence than control participants. Moreover, the temporal subdivision of the walking gait of people with PD is similar to healthy individuals but the coordination during the double support phase is different. Hence, this study indicates that isolating the speed factor is crucial in gait analysis for PD.
Collapse
Affiliation(s)
- Aurélien Patoz
- Institute of Sport Sciences, University of Lausanne, Lausanne, Switzerland
- Research and Development Department, Volodalen Swiss Sport Lab, Aigle, Switzerland
- *Correspondence: Aurélien Patoz,
| | - Davide Malatesta
- Institute of Sport Sciences, University of Lausanne, Lausanne, Switzerland
| | - Johannes Burtscher
- Institute of Sport Sciences, University of Lausanne, Lausanne, Switzerland
| |
Collapse
|
|
25
|
A type-2 neuro-fuzzy system with a novel learning method for Parkinson’s disease diagnosis. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04276-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
|
26
|
di Biase L, Raiano L, Caminiti ML, Pecoraro PM, Di Lazzaro V. Parkinson's Disease Wearable Gait Analysis: Kinematic and Dynamic Markers for Diagnosis. SENSORS (BASEL, SWITZERLAND) 2022; 22:8773. [PMID: 36433372 PMCID: PMC9693970 DOI: 10.3390/s22228773] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/02/2022] [Accepted: 11/07/2022] [Indexed: 06/16/2023]
Abstract
Introduction: Gait features differ between Parkinson's disease (PD) and healthy subjects (HS). Kinematic alterations of gait include reduced gait speed, swing time, and stride length between PD patients and HS. Stride time and swing time variability are increased in PD patients with respect to HS. Additionally, dynamic parameters of asymmetry of gait are significantly different among the two groups. The aim of the present study is to evaluate which kind of gait analysis (dynamic or kinematic) is more informative to discriminate PD and HS gait features. Methods: In the present study, we analyzed gait dynamic and kinematic features of 108 PD patients and 88 HS from four cohorts of two datasets. Results: Kinematic features showed statistically significant differences among PD patients and HS for gait speed and time Up and Go test and for selected kinematic dispersion indices (standard deviation and interquartile range of swing, stance, and double support time). Dynamic features did not show any statistically significant difference between PD patients and HS. Discussion: Despite kinematics features like acceleration being directly proportional to dynamic features like ground reaction force, the results of this study showed the so-called force/rhythm dichotomy since kinematic features were more informative than dynamic ones.
Collapse
Affiliation(s)
- Lazzaro di Biase
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy
- Brain Innovations Lab, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo 21, 00128 Rome, Italy
| | - Luigi Raiano
- NeXT: Neurophysiology and Neuroengineering of Human-Technology Interaction Research Unit, Campus Bio-Medico University, 00128 Rome, Italy
| | - Maria Letizia Caminiti
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy
| | - Pasquale Maria Pecoraro
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy
| | - Vincenzo Di Lazzaro
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy
| |
Collapse
|
|
27
|
Detecting Parkinson's Disease through Gait Measures Using Machine Learning. Diagnostics (Basel) 2022; 12:diagnostics12102404. [PMID: 36292093 PMCID: PMC9600300 DOI: 10.3390/diagnostics12102404] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 09/29/2022] [Accepted: 10/01/2022] [Indexed: 11/25/2022] Open
Abstract
Parkinson’s disease (PD) is one of the most common long-term degenerative movement disorders that affects the motor system. This progressive nervous system disorder affects nearly one million Americans, and more than 20,000 new cases are diagnosed each year. PD is a chronic and progressive painful neurological disorder and usually people with PD live 10 to 20 years after being diagnosed. PD is diagnosed based on the identification of motor signs of bradykinesia, rigidity, tremor, and postural instability. Though several attempts have been made to develop explicit diagnostic criteria, this is still largely unrevealed. In this manuscript, we aim to build a classifier with gait data from Parkinson patients and healthy controls using machine learning methods. The classifier could help facilitate a more accurate and cost-effective diagnostic method. The input to our algorithm is the Gait in Parkinson’s Disease dataset published on PhysioNet containing force sensor data as the measurement of gait from 92 healthy subjects and 214 patients with idiopathic Parkinson’s Disease. Different machine learning methods, including logistic regression, SVM, decision tree, KNN were tested to output a predicted classification of Parkinson patients and healthy controls. Baseline models including frequency domain method can reach similar performance and may be another good approach for the PD diagnostics.
Collapse
|
|
28
|
Okamoto K, Obayashi I, Kokubu H, Senda K, Tsuchiya K, Aoi S. Contribution of Phase Resetting to Statistical Persistence in Stride Intervals: A Modeling Study. Front Neural Circuits 2022; 16:836121. [PMID: 35814485 PMCID: PMC9257880 DOI: 10.3389/fncir.2022.836121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 05/19/2022] [Indexed: 11/13/2022] Open
Abstract
Stride intervals in human walking fluctuate from one stride to the next, exhibiting statistical persistence. This statistical property is changed by aging, neural disorders, and experimental interventions. It has been hypothesized that the central nervous system is responsible for the statistical persistence. Human walking is a complex phenomenon generated through the dynamic interactions between the central nervous system and the biomechanical system. It has also been hypothesized that the statistical persistence emerges through the dynamic interactions during walking. In particular, a previous study integrated a biomechanical model composed of seven rigid links with a central pattern generator (CPG) model, which incorporated a phase resetting mechanism as sensory feedback as well as feedforward, trajectory tracking, and intermittent feedback controllers, and suggested that phase resetting contributes to the statistical persistence in stride intervals. However, the essential mechanisms remain largely unclear due to the complexity of the neuromechanical model. In this study, we reproduced the statistical persistence in stride intervals using a simplified neuromechanical model composed of a simple compass-type biomechanical model and a simple CPG model that incorporates only phase resetting and a feedforward controller. A lack of phase resetting induced a loss of statistical persistence, as observed for aging, neural disorders, and experimental interventions. These mechanisms were clarified based on the phase response characteristics of our model. These findings provide useful insight into the mechanisms responsible for the statistical persistence of stride intervals in human walking.
Collapse
Affiliation(s)
- Kota Okamoto
- Department of Aeronautics and Astronautics, Graduate School of Engineering, Kyoto University, Kyoto Daigaku-Katsura, Kyoto, Japan
| | - Ippei Obayashi
- Cyber-Physical Engineering Information Research Core (Cypher), Okayama University, Okayama, Japan
| | - Hiroshi Kokubu
- Department of Mathematics, Graduate School of Science, Kyoto University, Kyoto, Japan
| | - Kei Senda
- Department of Aeronautics and Astronautics, Graduate School of Engineering, Kyoto University, Kyoto Daigaku-Katsura, Kyoto, Japan
| | - Kazuo Tsuchiya
- Department of Aeronautics and Astronautics, Graduate School of Engineering, Kyoto University, Kyoto Daigaku-Katsura, Kyoto, Japan
| | - Shinya Aoi
- Department of Mechanical Science and Bioengineering, Graduate School of Engineering Science, Osaka University, Osaka, Japan
- *Correspondence: Shinya Aoi
| |
Collapse
|
|
29
|
Kwok JYY, Smith R, Chan LML, Lam LCC, Fong DYT, Choi EPH, Lok KYW, Lee JJ, Auyeung M, Bloem BR. Managing freezing of gait in Parkinson's disease: a systematic review and network meta-analysis. J Neurol 2022; 269:3310-3324. [PMID: 35244766 DOI: 10.1007/s00415-022-11031-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 02/07/2022] [Accepted: 02/11/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND Freezing of gait (FOG) is one of the most disabling gait disorders affecting 80% of patients with Parkinson's disease (PD). Clinical guidelines recommend a behavioral approach for gait rehabilitation, but there is a wide diversity of behavioral modalities. OBJECTIVE The objective of this network meta-analysis was to compare the effectiveness of different behavioral interventions for FOG management in PD patients. METHODS Six databases were searched for randomized controlled trials of behavioral interventions for FOG management among PD patients from 1990 to December 2021. Bayesian network meta-analysis was used to combine both direct and indirect trial evidence on treatment effectiveness, while the surface under the cumulative ranking (SUCRA) score was used to estimate the ranked probability of intervention effectiveness. RESULTS Forty-six studies were included in the qualitative synthesis. Among, 36 studies (1454 patients) of 72 interventions or control conditions (12 classes) were included in the network meta-analysis, with a mean intervention period of 10.3 weeks. After adjusting for the moderating effect of baseline FOG severity, obstacle training [SMD -2.1; 95% credible interval (Crl): -3.3, -0.86], gait training with treadmill (SMD -1.2; 95% Crl: -2.0, -0.34), action observation training (SMD -1.0; 95% Crl: -1.9, -0.14), conventional physiotherapy (SMD -0.70; 95% Crl: -1.3, -0.12) and general exercise (SMD -0.64; 95% Crl: -1.2, -0.11) demonstrated significant improvement on immediate FOG severity compared to usual care. The SUCRA rankings suggest that obstacle training, gait training on treadmill and general exercises are most likely to reduce FOG severity. CONCLUSION Obstacle training, gait training on treadmill, general exercises, action observation training and conventional physiotherapy demonstrated immediate real-life benefits on FOG symptoms among patients with mild-moderate PD. With the promising findings, the sustained effects of high complexity motor training combined with attentional/cognitive strategy should be further explored. Future trials with rigorous research designs using both subjective and objective outcome measures, long-term follow-up and cost-effective analysis are warranted to establish effective behavioral strategies for FOG management.
Collapse
Affiliation(s)
- Jojo Yan Yan Kwok
- School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People's Republic of China.
| | - Robert Smith
- School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Lily Man Lee Chan
- School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Leo Chun Chung Lam
- School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, UK
| | - Daniel Yee Tak Fong
- School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Edmond Pui Hang Choi
- School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Kris Yuet Wan Lok
- School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Jung Jae Lee
- School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, People's Republic of China
| | - Man Auyeung
- Department of Medicine, Pamela Youde Nethersole Eastern Hospital, Chai Wan, Hong Kong SAR, People's Republic of China
| | - Bastiaan R Bloem
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| |
Collapse
|
|
30
|
Hollman JH, Lee WD, Ringquist DC, Taisey C, Ness DK. Comparing adaptive fractal and detrended fluctuation analyses of stride time variability: Tests of equivalence. Gait Posture 2022; 94:9-14. [PMID: 35189574 DOI: 10.1016/j.gaitpost.2022.02.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 02/12/2022] [Accepted: 02/14/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND Fractal analyses quantify self-similarities in stride-to-stride fluctuations over different time scales. Fractal exponents can be measured with adaptive fractal analysis (AFA) or detrended fluctuation analysis (DFA), though measurements obtained with the algorithms have not been directly compared. RESEARCH QUESTION Are stride time fractal exponents measured with AFA and DFA algorithms equivalent? METHODS Data from 50 participants with Parkinson's Disease (n = 15), age-similar healthy adults (n = 15) and healthy young adults (n = 20) were analyzed in this cross-sectional, observational study. Participants completed 6-min walks at self-selected speeds overground on a straight walkway and on a treadmill. Stride times were measured with inertial measurement units. Fractal exponents in stride time data were processed using AFA and DFA algorithms and compared with two one-sided tests of equivalence. Mixed ANOVAs were used to compare exponents between groups and conditions. RESULTS Fractal exponents computed with AFA and DFA were equivalent neither in the overground (0.796 & 0.830, respectively, p = .587) nor treadmill conditions (0.806 & 0.882, respectively, p = .122). Fractal exponents measured with DFA were higher than when measured with AFA. Standard errors were 22% lower when measured with AFA. Additionally, a group × condition interaction was statistically significant when fractal exponents were processed with the AFA algorithm (F(2,47) = 11.696, p < .001), whereas the group × condition interaction was not statistically significant when DFA exponents were compared (F(2, 47) = 2.144, p = .129). SIGNIFICANCE AFA and DFA do not produce equivalent estimates of the fractal exponent α in stride time dynamics. Estimates of the fractal exponent α obtained with AFA or DFA algorithms therefore should not be used interchangeably. Standard errors were lower when derived with AFA. Fractal exponents calculated with AFA may be more sensitive to conditions that influence stride time fractal dynamics than are measures calculated with DFA.
Collapse
Affiliation(s)
- John H Hollman
- Department of Physical Medicine & Rehabilitation, Mayo Clinic, Rochester, MN, USA; Program in Physical Therapy, Mayo Clinic School of Health Sciences, Mayo Clinic College of Medicine and Science, Rochester, MN, USA.
| | - Wakon D Lee
- Program in Physical Therapy, Mayo Clinic School of Health Sciences, Mayo Clinic College of Medicine and Science, Rochester, MN, USA
| | - Dane C Ringquist
- Program in Physical Therapy, Mayo Clinic School of Health Sciences, Mayo Clinic College of Medicine and Science, Rochester, MN, USA
| | - Corey Taisey
- Program in Physical Therapy, Mayo Clinic School of Health Sciences, Mayo Clinic College of Medicine and Science, Rochester, MN, USA
| | - Debra K Ness
- Department of Physical Medicine & Rehabilitation, Mayo Clinic, Rochester, MN, USA
| |
Collapse
|
|
31
|
Nagano H, Said CM, James L, Sparrow WA, Begg R. Biomechanical Correlates of Falls Risk in Gait Impaired Stroke Survivors. Front Physiol 2022; 13:833417. [PMID: 35330930 PMCID: PMC8940193 DOI: 10.3389/fphys.2022.833417] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 02/11/2022] [Indexed: 12/03/2022] Open
Abstract
Increased falls risk is prevalent among stroke survivors with gait impairments. Tripping is the leading cause of falls and it is highly associated with mid-swing Minimum Foot Clearance (MFC), when the foot’s vertical margin from the walking surface is minimal. The current study investigated MFC characteristics of post-stroke individuals (n = 40) and healthy senior controls (n = 21) during preferred speed treadmill walking, using an Optotrak 3D motion capture system to record foot-ground clearance. In addition to MFC, bi-lateral spatio-temporal gait parameters, including step length, step width and double support time, were obtained for the post-stroke group’s Unaffected and Affected limb and the control group’s Dominant and Non-dominant limbs. Statistical analysis of MFC included central tendency (mean, median), step-to-step variability (standard deviation and interquartile range) and distribution (skewness and kurtosis). In addition, the first percentile, that is the lowest 1% of MFC values (MFC 1%) were computed to identify very high-risk foot trajectory control. Spatio-temporal parameters were described using the mean and standard deviation with a 2 × 2 (Group × Limb) Multivariate Analysis of Variance applied to determine significant Group and Limb effects. Pearson’s correlations were used to reveal any interdependence between gait variables and MFC control. The main finding of the current research was that post-stroke group’s affected limb demonstrated lower MFC 1% with higher variability and lower kurtosis. Post-stroke gait was also characterised by shorter step length, larger step width and increased double support time. Gait retraining methods, such as using real-time biofeedback, would, therefore, be recommended for post-stroke individuals, allowing them to acquire optimum swing foot control and reduce their tripping risk by elevating the swing foot and improving step-to-step consistency in gait control.
Collapse
Affiliation(s)
- Hanatsu Nagano
- Institute for Health and Sports (IHeS), Victoria University, Melbourne, VIC, Australia
- *Correspondence: Hanatsu Nagano,
| | - Catherine M. Said
- Department of Physiotherapy, Melbourne School of Health Sciences, University of Melbourne, Melbourne, VIC, Australia
- Department of Physiotherapy, Western Health, St. Albans, VIC, Australia
- Australian Institute for Musculoskeletal Science, St. Albans, VIC, Australia
- Department of Physiotherapy, Austin Health, Heidelberg, VIC, Australia
| | - Lisa James
- Institute for Health and Sports (IHeS), Victoria University, Melbourne, VIC, Australia
| | - William A. Sparrow
- Institute for Health and Sports (IHeS), Victoria University, Melbourne, VIC, Australia
| | - Rezaul Begg
- Institute for Health and Sports (IHeS), Victoria University, Melbourne, VIC, Australia
| |
Collapse
|
|
32
|
Lu C, Louie KH, Twedell E, Vitek JL, MacKinnon CD, Cooper SE. Overground versus treadmill walking in Parkinson's disease: Relationship between speed and spatiotemporal gait metrics. Gait Posture 2022; 93:96-101. [PMID: 35121487 PMCID: PMC8930449 DOI: 10.1016/j.gaitpost.2022.01.020] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 11/08/2021] [Accepted: 01/24/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND Treadmills provide a safe and convenient way to study the gait of people with Parkinson's disease (PD), but outcome measures derived from treadmill gait may differ from overground walking. OBJECTIVE To investigate how the relationships between gait metrics and walking speed vary between overground and treadmill walking in people with PD and healthy controls. METHODS We compared 29 healthy controls to 27 people with PD in the OFF-medication state. Subjects first walked overground on an instrumented gait walkway, then on an instrumented treadmill at 85%, 100% and 115% of their overground walking speed. Average stride length and cadence were computed for each subject in both overground and treadmill walking. RESULTS Stride length and cadence both differed between overground and treadmill walking. Regressions of stride length and cadence on gait speed showed a log-log relationship for both overground and treadmill gait in both PD and control groups. The difference between the PD and control groups during overground gait was maintained for treadmill gait, not only when treadmill speed matched overground speed, but also with ± 15% variation in treadmill speed from that value. SIGNIFICANCE These results show that the impact of PD on stride length and cadence and their relationship to gait speed is preserved in treadmill as compared to overground walking. We conclude that a treadmill protocol is suitable for laboratory use in studies of PD gait therapeutics.
Collapse
Affiliation(s)
- Chiahao Lu
- Department of Neurology, University of Minnesota, 516 Delaware Street SE, Minneapolis, MN 55455, United States.
| | - Kenneth H Louie
- Department of Biomedical Engineering, University of Minnesota, 7-105 Hasselmo Hall 312 Church Street SE, Minneapolis, MN 55455, United States.,Present address: Department of Neurological Surgery, University of California, 513 Parnassus Ave, M779, San Francisco, CA 94143, United States
| | - Emily Twedell
- Department of Neurology, University of Minnesota, 516 Delaware Street SE, Minneapolis, MN 55455, United States.,Present address: Department of Neuroscience, University of California, 495 Nelson Rising Lane, San Francisco, CA 94158, United States
| | - Jerrold L. Vitek
- Department of Neurology, University of Minnesota, 516 Delaware Street SE, Minneapolis, MN 55455, United States
| | - Colum D MacKinnon
- Department of Neurology, University of Minnesota, 516 Delaware Street SE, Minneapolis, MN 55455, United States
| | - Scott E Cooper
- Department of Neurology, University of Minnesota, 516 Delaware Street SE, Minneapolis, MN 55455, United States
| |
Collapse
|
|
33
|
Novel machine learning-based hybrid strategy for severity assessment of Parkinson’s disorders. Med Biol Eng Comput 2022; 60:811-828. [DOI: 10.1007/s11517-022-02518-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 01/21/2022] [Indexed: 10/19/2022]
|
|
34
|
de Almeida FO, Santana V, Corcos DM, Ugrinowitsch C, Silva-Batista C. Effects of Endurance Training on Motor Signs of Parkinson's Disease: A Systematic Review and Meta-Analysis. Sports Med 2022; 52:1789-1815. [PMID: 35113386 DOI: 10.1007/s40279-022-01650-x] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/15/2022] [Indexed: 02/07/2023]
Abstract
BACKGROUND Evidence has demonstrated that endurance training (ET) reduces the motor signs of Parkinson's disease (PD). However, there has not been a comprehensive meta-analysis of studies to date. OBJECTIVE The aim of this study was to compare the effect of ET versus nonactive and active control conditions on motor signs as assessed by either the Unified Parkinson's Disease Rating Scale part III (UPDRS-III) or Movement Disorder Society-UPDRS-III (MDS-UPDRS-III). METHODS A random-effect meta-analysis model using standardized mean differences (Hedges' g) determined treatment effects. Moderators (e.g., combined endurance and physical therapy training [CEPTT]) and meta-regressors (e.g., number of sessions) were used for sub-analyses. Methodological quality was assessed by the Physiotherapy Evidence Database. RESULTS Twenty-seven randomized controlled trials (RCTs) met inclusion criteria (1152 participants). ET is effective in decreasing UPDRS-III scores when compared with nonactive and active control conditions (g = - 0.68 and g = - 0.33, respectively). This decrease was greater (within- and between-groups average of - 8.0 and - 6.8 point reduction on UPDRS-III scores, respectively) than the moderate range of clinically important changes to UPDRS-III scores (- 4.5 to - 6.7 points) suggested for PD. Although considerable heterogeneity was observed between RCTs (I2 = 74%), some moderators that increased the effect of ET on motor signs decreased the heterogeneity of the analyses, such as CEPTT (I2 = 21%), intensity based on treadmill speed (I2 = 0%), self-perceived exertion rate (I2 = 33%), and studies composed of individuals with PD and freezing of gait (I2 = 0%). Meta-regression did not produce significant relationships between ET dosage and UPDRS-III scores. CONCLUSIONS ET is effective in decreasing UPDRS-III scores. Questions remain about the dose-response relationship between ET and reduction in motor signs.
Collapse
Affiliation(s)
| | - Vagner Santana
- Exercise Neuroscience Research Group, University of São Paulo, São Paulo, Brazil
| | - Daniel M Corcos
- Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, IL, USA
| | - Carlos Ugrinowitsch
- Laboratory of Adaptations To Strength Training, School of Physical Education and Sport, University of São Paulo, São Paulo, Brazil
| | - Carla Silva-Batista
- Exercise Neuroscience Research Group, University of São Paulo, São Paulo, Brazil. .,School of Arts, Sciences and Humanities of University of São Paulo, St. Arlindo Béttio, 1000, 03828-000, Vila Guaraciaba, São Paulo, Brazil. .,Laboratory of Adaptations To Strength Training, School of Physical Education and Sport, University of São Paulo, São Paulo, Brazil.
| |
Collapse
|
|
35
|
Ferrazzoli D, Ortelli P, Iansek R, Volpe D. Rehabilitation in movement disorders: From basic mechanisms to clinical strategies. HANDBOOK OF CLINICAL NEUROLOGY 2022; 184:341-355. [PMID: 35034747 DOI: 10.1016/b978-0-12-819410-2.00019-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Movement disorders encompass a variety of conditions affecting the nervous system at multiple levels. The pathologic processes underlying movement disorders alter the normal neural functions and could lead to aberrant neuroplastic changes and to clinical phenomenology that is not expressed only through mere motor symptoms. Given this complexity, the responsiveness to pharmacologic and surgical therapies is often disappointing. Growing evidence supports the efficacy of neurorehabilitation for the treatment of movement disorders. Specific form of training involving both goal-based practice and aerobic training could drive and modulate neuroplasticity in order to restore the circuitries dysfunctions and to achieve behavioral gains. This chapter provides an overview of the alterations expressed in some movement disorders in terms of clinical signs and symptoms and plasticity, and suggests which ones and why tailored rehabilitation strategies should be adopted for the management of the different movement disorders.
Collapse
Affiliation(s)
- Davide Ferrazzoli
- Department of Neurorehabilitation, Hospital of Vipiteno (SABES-ASDAA), Vipiteno-Sterzing, Italy; Department of Parkinson's Disease, Fresco Parkinson Center, Movement Disorders and Brain Injury Rehabilitation, "Moriggia-Pelascini" Hospital-Gravedona ed Uniti, Como, Italy
| | - Paola Ortelli
- Department of Parkinson's Disease, Fresco Parkinson Center, Movement Disorders and Brain Injury Rehabilitation, "Moriggia-Pelascini" Hospital-Gravedona ed Uniti, Como, Italy; Department of Parkinson's Disease, Fresco Parkinson Center, Movement Disorders and Brain Injury Rehabilitation, "Moriggia-Pelascini" Hospital-Gravedona ed Uniti, Como, Italy
| | - Robert Iansek
- Clinical Research Centre for Movement Disorders and Gait, National Parkinson Foundation Center of Excellence, Monash Health, Cheltenham, VIC, Australia; School of Clinical Sciences, Monash University, Clayton, VIC, Australia
| | - Daniele Volpe
- Department of Rehabilitation, Fresco Parkinson Center, Villa Margherita, S. Stefano Riabilitazione, Vicenza, Italy
| |
Collapse
|
|
36
|
Lockwich J, Schwartzkopf-Phifer K, Skubik-Peplaski C, Andreatta RD, Kitzman P. Perceived exercise habits of individuals with Parkinson’s disease living in the community. Clin Park Relat Disord 2022; 6:100127. [PMID: 35005604 PMCID: PMC8719012 DOI: 10.1016/j.prdoa.2021.100127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 11/30/2021] [Accepted: 12/08/2021] [Indexed: 11/29/2022] Open
Abstract
Context Exercise has been shown to improve gait in individuals with Parkinson’s disease (PD). Stepping practice at higher intensity levels has been suggested as a beneficial treatment option to improve gait in the neurological population. Unfortunately, this mode is poorly understood and underutilized within the PD population. Information on what individuals with PD are doing for exercise would be beneficial to help tailor exercise programs to improve gait and provide exercise options in the community for intensity-based exercise. Objective To investigate the current exercise habits of individuals living with PD in the community aimed at improving walking and to understand the impact of perceived intensity on daily exercise practices. Design, setting, participants One hundred thirty-eight individuals with PD living in the community were surveyed online regarding their current exercise habits. Main outcome measure A total of 22 questions aimed to understand exercise selection, focus, and perceived intensity. Questions asked basic demographic, symptom presentation and management of disease related symptoms that were present while living with PD. Exercise questions focused understanding participants current function level, practice exercise habits and perceived levels of exercise intensity during daily routines. Results Of the 138 individuals surveyed for this preliminary study, eighty-seven percent of individuals with PD participated in exercise with seventy-five percent choosing walking as a mode for exercise. Sixty-five percent of the respondents noted that despite exercise, their walking speed and endurance has worsened since diagnosis. Eighty-one percent perceived exercising at moderate intensity levels, however little provocation of intensity symptoms was noted. Conclusion Our preliminary study survey results suggest that individuals with PD are exercising but not at high enough intensity levels to promote improvements in gait performance. Individuals with PD may need to be pushed at higher intensity levels, beyond their voluntary limits, to induce gait performance changes. These findings can provide a foundation for future fitness interventions within this population to target improving gait.
Collapse
Affiliation(s)
- Jordana Lockwich
- University of Evansville, Evansville IN 47722, USA
- Corresponding author at: Stone Family Center of Health Sciences, 515 Walnut Street, Evansville, IN 47708, USA.
| | | | | | | | | |
Collapse
|
|
37
|
Zanin M, Olivares F, Pulido-Valdeolivas I, Rausell E, Gomez-Andres D. Gait analysis under the lens of statistical physics. Comput Struct Biotechnol J 2022; 20:3257-3267. [PMID: 35782747 PMCID: PMC9237948 DOI: 10.1016/j.csbj.2022.06.022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/10/2022] [Accepted: 06/10/2022] [Indexed: 11/29/2022] Open
Abstract
Human gait is a fundamental activity, essential for the survival of the individual, and an emergent property of the interactions between complex physical and cognitive processes. Gait is altered in many situations, due both to external constraints, as e.g. paced walk, and to physical and neurological pathologies. Its study is therefore important as a way of improving the quality of life of patients, but also as a door to understanding the inner working of the human nervous system. In this review we explore how four statistical physics concepts have been used to characterise normal and pathological gait: entropy, maximum Lyapunov exponent, multi-fractal analysis and irreversibility. Beyond some basic definitions, we present the main results that have been obtained in this field, as well as a discussion of the main limitations researchers have dealt and will have to deal with. We finally conclude with some biomedical considerations and avenues for further development.
Collapse
Affiliation(s)
- Massimiliano Zanin
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, Palma de Mallorca 07122, Spain
| | - Felipe Olivares
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, Palma de Mallorca 07122, Spain
| | - Irene Pulido-Valdeolivas
- Department of Anatomy, Histology and Neuroscience, School of Medicine, Universidad Autónoma de Madrid, Calle del Arzobispo Morcillo 2, Madrid 28029, Spain
| | - Estrella Rausell
- Department of Anatomy, Histology and Neuroscience, School of Medicine, Universidad Autónoma de Madrid, Calle del Arzobispo Morcillo 2, Madrid 28029, Spain
| | - David Gomez-Andres
- Department of Anatomy, Histology and Neuroscience, School of Medicine, Universidad Autónoma de Madrid, Calle del Arzobispo Morcillo 2, Madrid 28029, Spain
- Pediatric Neurology, Vall d'Hebron Institut de Recerca (VHIR), Hospital Universitari Vall d'Hebron, Vall d'Hebron Barcelona Hospital Campus, ERN-RND & EURO-NMD, Pg. de la Vall d'Hebron 119-129, Barcelona 08035, Spain
| |
Collapse
|
|
38
|
Rohafza M, Soangra R, Smith JA, Ignasiak NK. Self-paced treadmills do not allow for valid observation of linear and nonlinear gait variability outcomes in patients with Parkinson's disease. Gait Posture 2022; 91:35-41. [PMID: 34634614 DOI: 10.1016/j.gaitpost.2021.10.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 10/04/2021] [Accepted: 10/06/2021] [Indexed: 02/02/2023]
Abstract
BACKGROUND Due to the imposed constant belt speed, motorized treadmills are known to affect linear and nonlinear gait variability outcomes. This is particularly true of patients with Parkinson's Disease where the treadmill can act as an external pacemaker. Self-paced treadmills update the belt speed in response to the subject's walking speed and might, therefore, be a useful tool for measurement of gait variability in this patient population. This study aimed to compare gait variability during walking at self-paced and constant treadmill speeds with overground walking in individuals with PD and individuals with unimpaired gait. METHODS Thirteen patients with Parkinson's Disease and thirteen healthy controls walked under three conditions: overground, on a treadmill at a constant speed, and using three self-paced treadmill modes. Gait variability was assessed with coefficient of variation (CV), sample entropy (SampEn), and detrended fluctuation analysis (DFA) of stride time and length. Systematic and random error between the conditions was quantified. RESULTS For individuals with PD, error in variability measurement was less during self-paced modes compared with constant treadmill speed for stride time but not for stride length. However, there was substantial error for stride time and length variability for all treadmill conditions. For healthy controls the error in measurement associated with treadmill walking was substantially less. SIGNIFICANCE The large systematic and random errors between overground and treadmill walking prohibit meaningful gait variability observations in patients with Parkinson's Disease using self-paced or constant-speed treadmills.
Collapse
Affiliation(s)
- Maryam Rohafza
- Department of Physical Therapy, Crean College of Health and Behavioral Sciences, Chapman University, Orange, CA, 92866, USA
| | - Rahul Soangra
- Department of Physical Therapy, Crean College of Health and Behavioral Sciences, Chapman University, Orange, CA, 92866, USA; Department of Electrical and Computer Science Engineering, Fowler School of Engineering, Chapman University, Orange, CA, 92866, USA.
| | - Jo Armour Smith
- Department of Physical Therapy, Crean College of Health and Behavioral Sciences, Chapman University, Orange, CA, 92866, USA
| | - Niklas König Ignasiak
- Department of Electrical and Computer Science Engineering, Fowler School of Engineering, Chapman University, Orange, CA, 92866, USA
| |
Collapse
|
|
39
|
Bange M, Gonzalez-Escamilla G, Lang NSC, Ding H, Radetz A, Herz DM, Schöllhorn WI, Muthuraman M, Groppa S. Gait Abnormalities in Parkinson's Disease Are Associated with Extracellular Free-Water Characteristics in the Substantia Nigra. JOURNAL OF PARKINSON'S DISEASE 2022; 12:1575-1590. [PMID: 35570500 DOI: 10.3233/jpd-223225] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
BACKGROUND Gait impairments are common in Parkinson's disease (PD). The pathological mechanisms are complex and not thoroughly elucidated, thus quantitative and objective parameters that closely relate to gait characteristics are critically needed to improve the diagnostic assessments and monitor disease progression. The substantia nigra is a relay structure within basal ganglia brainstem loops that is centrally involved in gait modulation. OBJECTIVE We tested the hypothesis that quantitative gait biomechanics are related to the microstructural integrity of the substantia nigra and PD-relevant gait abnormalities are independent from bradykinesia-linked speed reductions. METHODS Thirty-eight PD patients and 33 age-matched control participants walked on a treadmill at fixed speeds. Gait parameters were fed into a principal component analysis to delineate relevant features. We applied the neurite orientation dispersion and density imaging (NODDI) model on diffusion-weighted MR-images to calculate the free-water content as an advanced marker of microstructural integrity of the substantia nigra and tested its associations with gait parameters. RESULTS Patients showed increased duration of stance phase, load response, pre-swing, and double support time, as well as reduced duration of single support and swing time. Gait rhythmic alterations associated positively with the free-water content in the right substantia nigra in PD, indicating that patients with more severe neurodegeneration extend the duration of stance phase, load response, and pre-swing. CONCLUSION The results provide evidence that gait alterations are not merely a byproduct of bradykinesia-related reduced walking speed. The data-supported association between free-water and the rhythmic component highlights the potential of substantia nigra microstructure imaging as a measure of gait-dysfunction and disease-progression.
Collapse
Affiliation(s)
- Manuel Bange
- Section of Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Gabriel Gonzalez-Escamilla
- Section of Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Nadine Sandra Claudia Lang
- Section of Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Hao Ding
- Section of Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Angela Radetz
- Section of Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Damian Marc Herz
- Section of Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
- MRC Brain Network Dynamics Unit at the University of Oxford, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | | | - Muthuraman Muthuraman
- Section of Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Sergiu Groppa
- Section of Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| |
Collapse
|
|
40
|
Gait based Parkinson’s disease diagnosis and severity rating using multi-class support vector machine. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107939] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
|
|
41
|
Loh HW, Hong W, Ooi CP, Chakraborty S, Barua PD, Deo RC, Soar J, Palmer EE, Acharya UR. Application of Deep Learning Models for Automated Identification of Parkinson's Disease: A Review (2011-2021). SENSORS 2021; 21:s21217034. [PMID: 34770340 PMCID: PMC8587636 DOI: 10.3390/s21217034] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 10/07/2021] [Accepted: 10/19/2021] [Indexed: 12/18/2022]
Abstract
Parkinson’s disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.
Collapse
Affiliation(s)
- Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
| | - Wanrong Hong
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
| | - Subrata Chakraborty
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Prabal Datta Barua
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
- School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
| | - Ravinesh C Deo
- School of Sciences, University of Southern Queensland, Springfield, QLD 4300, Australia
| | - Jeffrey Soar
- School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
| | - Elizabeth E Palmer
- Centre of Clinical Genetics, Sydney Children's Hospitals Network, Randwick, NSW 2031, Australia
- School of Women's and Children's Health, University of New South Wales, Randwick, NSW 2031, Australia
| | - U Rajendra Acharya
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
- School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
- School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung 413, Taiwan
- Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto 860-8555, Japan
| |
Collapse
|
|
42
|
Analysis of the stance phase of the gait cycle in Parkinson's disease and its potency for Parkinson's disease discrimination. J Biomech 2021; 129:110818. [PMID: 34736084 DOI: 10.1016/j.jbiomech.2021.110818] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Revised: 10/04/2021] [Accepted: 10/12/2021] [Indexed: 11/24/2022]
Abstract
In this study, using vertical ground reaction force (VGRF) data and focusing on the stance phase of the gait cycle, the effect of Parkinson's disease (PD) on gait was investigated. The used dataset consisted of 93 PD and 72 healthy individuals. Multiple comparisons correction ANOVA test and student t-test were used for statistical analyses. Results showed that a longer stance duration with a larger VGRF peak value (p < 0.05) was observed for PD patients during the stance phase. In addition, the VGRF peak value was delayed and blunted in PD cases compared with healthy individuals. These results indicated more time and effort for PD patients for posture stabilization during the stance phase. The time delay for different locations of the foot sole to contact the ground during the stance phase indicated that PD patients might use a different strategy for maintaining their body stability compared with healthy individuals. Although the VGRF time-domain pattern during the stance phase in PD was similar to healthy conditions, its local characteristics like duration and peak value differed significantly. The classification analysis based on the VGRF time-domain extracted features during the stance phase obtained PD recognition with accuracy, sensitivity and specificity of 90.82%, 88.63% and 82.56%, respectively.
Collapse
|
|
43
|
A dual-branch model for diagnosis of Parkinson’s disease based on the independent and joint features of the left and right gait. APPL INTELL 2021. [DOI: 10.1007/s10489-020-02182-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
|
|
44
|
Sosnik R, Danziger-Schragenheim S, Possti D, Fahoum F, Giladi N, Hausdorff JM, Mirelman A, Maidan I. Impaired Inhibitory Control During Walking in Parkinson's Disease Patients: An EEG Study. JOURNAL OF PARKINSONS DISEASE 2021; 12:243-256. [PMID: 34569972 DOI: 10.3233/jpd-212776] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The performance on a visual Go/NoGo (VGNG) task during walking has been used to evaluate the effect of gait on response inhibition in young and older adults; however, no work has yet included Parkinson's disease (PD) patients for whom such changes may be even more enhanced. OBJECTIVE In this study, we aimed to explore the effect of gait on automatic and cognitive inhibitory control phases in PD patients and the associated changes in neural activity and compared them with young and older adults. METHODS 30 PD patients, 30 older adults, and 11 young adults performed a visual Go/NoGo task in a sitting position and during walking on a treadmill while their EEG activity and gait were recorded. Brain electrical activity was evaluated by the amplitude, latency, and scalp distribution of N2 and P300 event related potentials. Mix model analysis was used to examine group and condition effects on task performance and brain activity. RESULTS The VGNG accuracy rates in PD patients during walking were lower than in young and older adults (F = 5.619, p = 0.006). For all groups, N2 latency during walking was significantly longer than during sitting (p = 0.013). In addition, P300 latency was significantly longer in PD patients (p < 0.001) and older adults (p = 0.032) during walking compared to sitting and during 'NoGo' trials compared with 'Go' trials. Moreover, the young adults showed the smallest number of electrodes for which a significant differential activation between sit to walk was observed, while PD patients showed the largest with N2 being more strongly manifested in bilateral parietal electrodes during walking and in frontocentral electrodes while seated. CONCLUSION The results show that response inhibition during walking is impaired in older subjects and PD patients and that increased cognitive load during dual-task walking relates to significant change in scalp electrical activity, mainly in parietal and frontocentral channels.
Collapse
Affiliation(s)
- Ronen Sosnik
- Faculty of Electrical Engineering, Holon Institute of Technology (H.I.T.), Holon, Israel
| | - Shani Danziger-Schragenheim
- Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Daniel Possti
- Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Israel
| | - Firas Fahoum
- Epilepsy and EEG Unit, Neurological Institute, Tel Aviv Sourasky Medical Center, Israel.,Department of Neurology, Sackler School of Medicine, Tel Aviv University, Israel
| | - Nir Giladi
- Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.,Epilepsy and EEG Unit, Neurological Institute, Tel Aviv Sourasky Medical Center, Israel.,Department of Neurology, Sackler School of Medicine, Tel Aviv University, Israel
| | - Jeffrey M Hausdorff
- Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Israel.,Department of Physical Therapy, Sackler Faculty of Medicine, Tel Aviv University, Israel.,Alzheimer's Disease Center and Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL, USA
| | - Anat Mirelman
- Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.,Department of Neurology, Sackler School of Medicine, Tel Aviv University, Israel
| | - Inbal Maidan
- Laboratory of Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.,Department of Neurology, Sackler School of Medicine, Tel Aviv University, Israel
| |
Collapse
|
|
45
|
E. B, D. B, Elumalai VK, R. V. Automatic and non-invasive Parkinson’s disease diagnosis and severity rating using LSTM network. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107463] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
|
|
46
|
Zhao H, Cao J, Wang R, Lei Y, Liao WH, Cao H. Accurate identification of Parkinson’s disease by distinctive features and ensemble decision trees. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102860] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
|
|
47
|
Local Pattern Transformation Based Feature Extraction for Recognition of Parkinson's Disease Based on Gait Signals. Diagnostics (Basel) 2021; 11:diagnostics11081395. [PMID: 34441329 PMCID: PMC8391513 DOI: 10.3390/diagnostics11081395] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 07/26/2021] [Accepted: 07/29/2021] [Indexed: 01/14/2023] Open
Abstract
Parkinson’s disease (PD) is a neuro-degenerative disorder primarily triggered due to the deterioration of dopamine-producing neurons in the substantia nigra of the human brain. The early detection of Parkinson’s disease can assist in preventing deteriorating health. This paper analyzes human gait signals using Local Binary Pattern (LBP) techniques during feature extraction before classification. Supplementary to the LBP techniques, Local Gradient Pattern (LGP), Local Neighbour Descriptive Pattern (LNDP), and Local Neighbour Gradient Pattern (LNGP) were utilized to extract features from gait signals. The statistical features were derived and analyzed, and the statistical Kruskal–Wallis test was carried out for the selection of an optimal feature set. The classification was then carried out by an Artificial Neural Network (ANN) for the identified feature set. The proposed Symmetrically Weighted Local Neighbour Gradient Pattern (SWLNGP) method achieves a better performance, with 96.28% accuracy, 96.57% sensitivity, and 95.94% specificity. This study suggests that SWLNGP could be an effective feature extraction technique for the recognition of Parkinsonian gait.
Collapse
|
|
48
|
Implementation of a Deep Learning Algorithm Based on Vertical Ground Reaction Force Time-Frequency Features for the Detection and Severity Classification of Parkinson's Disease. SENSORS 2021; 21:s21155207. [PMID: 34372444 PMCID: PMC8347971 DOI: 10.3390/s21155207] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 07/29/2021] [Accepted: 07/30/2021] [Indexed: 11/16/2022]
Abstract
Conventional approaches to diagnosing Parkinson’s disease (PD) and rating its severity level are based on medical specialists’ clinical assessment of symptoms, which are subjective and can be inaccurate. These techniques are not very reliable, particularly in the early stages of the disease. A novel detection and severity classification algorithm using deep learning approaches was developed in this research to classify the PD severity level based on vertical ground reaction force (vGRF) signals. Different variations in force patterns generated by the irregularity in vGRF signals due to the gait abnormalities of PD patients can indicate their severity. The main purpose of this research is to aid physicians in detecting early stages of PD, planning efficient treatment, and monitoring disease progression. The detection algorithm comprises preprocessing, feature transformation, and classification processes. In preprocessing, the vGRF signal is divided into 10, 15, and 30 s successive time windows. In the feature transformation process, the time domain vGRF signal in windows with varying time lengths is modified into a time–frequency spectrogram using a continuous wavelet transform (CWT). Then, principal component analysis (PCA) is used for feature enhancement. Finally, different types of convolutional neural networks (CNNs) are employed as deep learning classifiers for classification. The algorithm performance was evaluated using k-fold cross-validation (kfoldCV). The best average accuracy of the proposed detection algorithm in classifying the PD severity stage classification was 96.52% using ResNet-50 with vGRF data from the PhysioNet database. The proposed detection algorithm can effectively differentiate gait patterns based on time–frequency spectrograms of vGRF signals associated with different PD severity levels.
Collapse
|
|
49
|
Koyanagi Y, Fukushi I, Nakamura M, Suzuki K, Oda N, Aita T, Seki H. The effect of body weight-supported overground gait training for patients with Parkinson's disease: A retrospective case-control observational study. PLoS One 2021; 16:e0254415. [PMID: 34283843 PMCID: PMC8291710 DOI: 10.1371/journal.pone.0254415] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Accepted: 06/25/2021] [Indexed: 11/28/2022] Open
Abstract
OBJECTIVE To evaluate the effects of body weight-supported overground gait training (BWSOGT) on motor abilities, such as gait and balance, in patients with Parkinson's disease (PD). DESIGN Retrospective case-controlled observational study with a 4-week follow-up. SETTING Inpatient rehabilitation. PARTICIPANTS We selected 37 of 68 patients with PD. Inclusion criteria were (1) Hoehn & Yahr stage II-IV, (2) no medication adjustment during the study period, (3) at least 1 week since last medication adjustment, and (4) ability to walk more than 10 meters on their own. Exclusion criteria were (1) cerebrovascular disease or other complications affecting movement, (2) difficulty in measurement, (3) early discharge, (4) medication change during the study, and (5) development of complications. INTERVENTIONS Patients were divided into two groups. Patients in Group I underwent 20 minutes of BWSOGT with a mobile hoist in addition to the standard exercises; Group II performed 20 minutes of gait training in place of BWSOGT. In both groups, training was performed for a total of 15 times/4 weeks. MAIN OUTCOME MEASURE(S) Participants were evaluated using the Unified Parkinson's Disease Rating Scale total, part II, and part III; 10-m walk test; velocity; stride length; 6-minute walk test; timed up and go test; Berg Balance Scale; and freezing of gait before and after the intervention. RESULTS There were significant decreases in the Unified Parkinson's Disease Rating Scale total, part II, and part III in both groups; however, 6-minute walk test, timed up and go test, and freezing of gait results only improved in Group I. CONCLUSIONS BWSOGT for patients with PD improves gait ability and dynamic balance more than standard gait training.
Collapse
Affiliation(s)
- Yasuki Koyanagi
- Department of Rehabilitation, National Hospital Organization Sendai Medical Center, Sendai, Japan
- Department of Neurology and Rehabilitation, National Hospital Organization Iwaki Hospital, Iwaki, Japan
| | - Isato Fukushi
- Faculty of Health Sciences, Uekusa Gakuen University, Chiba, Japan
- Clinical Research Center, Murayama Medical Center, Musashimurayama, Japan
| | - Masatoshi Nakamura
- Institute for Human Movement and Medical Sciences, Niigata University of Health and Welfare, Niigata, Japan
| | - Kouji Suzuki
- Department of Neurology and Rehabilitation, National Hospital Organization Iwaki Hospital, Iwaki, Japan
| | - Nobuhito Oda
- Department of Neurology and Rehabilitation, National Hospital Organization Iwaki Hospital, Iwaki, Japan
| | - Takashi Aita
- Department of Neurology and Rehabilitation, National Hospital Organization Iwaki Hospital, Iwaki, Japan
| | - Hareaki Seki
- Department of Neurology and Rehabilitation, National Hospital Organization Iwaki Hospital, Iwaki, Japan
| |
Collapse
|
|
50
|
Kegelmeyer DA, Kostyk SK, Fritz NE, Scharre DW, Young GS, Tan Y, Schubert R, Reilmann R, Kloos AD. Immediate effects of treadmill walking in individuals with Lewy body dementia and Huntington's disease. Gait Posture 2021; 86:186-191. [PMID: 33756407 DOI: 10.1016/j.gaitpost.2021.03.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 01/11/2021] [Accepted: 03/07/2021] [Indexed: 02/02/2023]
Abstract
BACKGROUND Treadmill training may improve gait disorders associated with neurodegenerative diseases. In Parkinson's disease (PD), treadmill training alters gait patterns after one session, and long-term training improves gait parameters, fall risk, and quality of life. RESEARCH QUESTION What is the feasibility and safety of using this intervention for people with Lewy body dementia (LBD) or Huntington's disease (HD)? METHODS In this observational study, 10 individuals with HD, 8 individuals with LBD, and 10 control individuals walked for 20 min on a treadmill using a speed dependent protocol starting at a slow comfortable speed and increasing incrementally toward their normal overground speed. Feasibility was determined by compliance to protocol and safety by no incidents of abnormal vital signs or expressions of distress. Changes in gait measures, Timed Up and Go (TUG) scores and quantitative motor function measures (Q-Motor; precision grasp force variability, finger and foot tapping frequency) before and after treadmill walking were analyzed using linear models. RESULTS Treadmill training is feasible and safe in LBD and HD; although, participants could not initiate treadmill walking at their comfortable overground speeds, and only 3 participants with HD were able to achieve their overground walking speed within the 20-minute session. No changes in gait measures, TUG times, and Q-Motor measures were found among LBD and HD participants after treadmill walking, although control participants demonstrated significant increases in several gait measures, and foot tap frequency (estimated difference = 0.290; p = 0.026). SIGNIFICANCE Longer and more frequent treadmill sessions may be needed to see gait and motor function effects in LBD and HD. Motor and cognitive impairments associated with these diseases may make them less amenable to the effects of treadmill training.
Collapse
Affiliation(s)
- Deb A Kegelmeyer
- The Ohio State University, College of Medicine, Division of Physical Therapy, Columbus, OH, United States.
| | - Sandra K Kostyk
- The Ohio State University, College of Medicine, Department of Neurology, Columbus, OH, United States; The Ohio State University, College of Medicine, Department of Neuroscience, Columbus, OH, United States.
| | - Nora E Fritz
- The Ohio State University, College of Medicine, Division of Physical Therapy, Columbus, OH, United States.
| | - Douglas W Scharre
- The Ohio State University, College of Medicine, Department of Neurology, Columbus, OH, United States.
| | - Gregory S Young
- The Ohio State University, Center for Biostatistics, Columbus, OH, United States.
| | - Yubo Tan
- The Ohio State University, Center for Biostatistics, Columbus, OH, United States.
| | | | - Ralf Reilmann
- George Huntington Institute, Technology Park, Germany; Dept. of Radiology, University of Muenster, Muenster, Germany; Department of Neurodegenerative Diseases and Hertie-Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany.
| | - Anne D Kloos
- The Ohio State University, College of Medicine, Division of Physical Therapy, Columbus, OH, United States.
| |
Collapse
|