Gait quality indices, such as the Gillette Gait Index or Gait Profile Score (GPS), can provide clinicians with objective, straightforward measures to quantify gait pathology and monitor changes over time. However, these methods often require motion capture or stationary gait analysis systems, limiting their accessibility. Inertial sensors offer a portable, cost-effective alternative for gait analysis. This study aimed to evaluate a novel hidden Markov model-based similarity measure (HMM-SM) for assessing gait quality directly from gyroscope and accelerometer data captured by inertial sensors.

Walking trials were conducted with 26 lower-limb prosthetic users and 30 able-bodied individuals, using inertial sensors placed at various lower body locations. We computed the HMM-SM score along with other established inertial sensor-based methods, including the Movement Deviation Profile, Dynamic Time Warping, IMU-based Gait Normalcy Index, and Multifeature Gait Score. Spearman correlations with the GPS, a validated measure of gait quality, were assessed, as well as correlations among the inertial sensor methods. Welch's t-tests were used to evaluate the ability to distinguish between prosthetic subgroups.

The HMM-SM and other inertial sensor-based methods demonstrated moderate-to-strong correlations with the GPS (0.49 <|r|< 0.77 for significant correlations). Comparisons between different measures highlighted key similarities and differences, both in correlations and in their ability to differentiate between subgroups. Overall, the pelvis and lower leg sensors achieved significant correlations and outperformed the upper leg sensors, which did not achieve significant correlations with the GPS for any of the signal-based measures.

Results suggest inertial sensors located at the pelvis and lower leg provide valid markers for monitoring overall gait quality, offering the potential to develop nonobtrusive, wearable systems to facilitate long-term monitoring. Such systems could enhance rehabilitation by enabling continuous gait assessment that can be easily integrated in clinical and everyday settings.

Gait analysis is crucial for diagnosing and monitoring various healthcare conditions, but traditional marker-based motion capture (MoCap) systems require expensive equipment, extensive setup, and trained personnel, limiting their accessibility in clinical and home settings. Markerless systems reduce setup complexity but often require multiple cameras, fixed calibration, and are not designed for widespread clinical adoption. This study introduces 3DGait, an artificial intelligence-enhanced markerless 3-Dimensional gait analysis system that operates with a single consumer-grade depth camera, providing a streamlined, accessible alternative. The system integrates advanced machine learning algorithms to produce 49 angular, spatial, and temporal gait biomarkers commonly used in mobility analysis. We validated 3DGait against a marker-based MoCap (OptiTrack) using 16 trials from 8 healthy adults performing the Timed Up and Go (TUG) test. The system achieved an overall average mean absolute error (MAE) of 2.3°, with all MAE under 5.2°, and a Pearson's correlation coefficient (PCC) of 0.75 for angular biomarkers. All spatiotemporal biomarkers had errors no greater than 15 %. Temporal biomarkers (excluding TUG time) had errors under 0.03 s, corresponding to one video frame at 30 frames per second. These results demonstrate that 3DGait provides clinically acceptable gait metrics relative to marker-based MoCap, while eliminating the need for markers, calibration, or fixed camera placement. 3DGait's accessible, non-invasive and single camera design makes it practical for use in non-specialist clinics and home settings, supporting patient monitoring and chronic disease management. Future research will focus on validating 3DGait with diverse populations, including individuals with gait abnormalities, to broaden its clinical applications.

The gait analysis has been applied in many fields, such as the assessment of falling, force evaluation in sports, and gait disorder detection for neuromuscular diseases. Its main recording techniques include video cameras and wearable sensors. However, the present methods involve measuring surface electromyograms (sEMGs) to analyze muscle activities. The primary goal of this study is to estimate gait parameters under different power capacity of muscle by sEMGs measured from lower limbs. A self-made wireless device recorded sEMGs from two muscles of each foot, and GaitUp Physilog®5 sensors captured gait parameters from 18 participants under running as references. Four features including median frequency (MDF), waveform length (WL), standard deviation (SD), and sample entropy (SampEn), were extracted from the sEMG data. The analysis utilized three machine learning models (Random Forest, CatBoost, XGBoost), evaluated through various evaluation metrics. Additionally, 5-fold cross-validation was conducted to assess the influence of muscle fatigue on the estimation of gait parameters. The results show that all models successfully estimated 20 gait parameters, all showing a Pearson correlation coefficient (PCC) above 0.800. However, the performance of models significantly depends on the condition of muscle fatigue. This study represents a significant advancement in gait analysis, providing a comprehensive method for estimating gait parameters from sEMG signals, with important implications for mobile health applications.

The Ten Meter Walk Test (10MWT) is a vital diagnostic tool for identifying neuromuscular and neurodegenerative conditions. This systematic review explores the potential of wearables, mobile devices, and sensors to enhance the 10MWT's use in medical gait analysis based on lower limb movements.

This systematic review explores the use of wearables, mobile devices, and sensors to improve the 10MWT in medical gait analysis based on lower limb movements. The study uses the PRISMA approach to assess literature from January 2010 to October 2023, highlighting the importance of new technologies like machine learning and artificial intelligence in improving the accuracy and efficiency of the 10MWT.

The findings demonstrate how technology-enabled 10MWT can help develop specialized treatment strategies and provide a more accurate understanding of disease pathophysiology.

The paper reviews 17 studies on lower limb movements during the 10MWT, highlighting their importance in assessing medical diseases and gait analysis as a diagnostic tool. It emphasizes the role of technology in rehabilitation and physical therapy, where some studies combine Transcranial Direct Current Stimulation with robotic or wearable technologies.

The review comprehensively explains these technologies' advantages and current use in therapeutic contexts.

Gait analysis is widely utilized for the diagnosis and prognosis of various diseases. Recently, innovative convenient markerless motion capture systems have been developed to replace the traditional marker-based three-dimensional motion capture systems.

s:This study is to evaluate the test-retest reliability of a novel video-based markerless motion capture system(Watrix, China) and to assess its concordance with a three-dimensional motion analysis system (BTS, Italy) in a population of young healthy subjects.

Our study included 36 healthy adult participants. Each subject underwent three assessments using Watrix system and BTS system. To evaluate the validity and reliability of the measurements, we employed paired-sample t-tests, Wilcoxon signed-rank tests, intra-class correlation coefficients, Bland-Altman analysis and Passing Bablok regression analysis.

Both intra-rater and inter-rater reliability demonstrated moderate to excellent correlations, with intraclass correlation coefficient (ICC) values ranging from 0.507 to 0.936, except for cadence(ICC = 0.233). The validity exhibited a good correlation for sagittal plane parameters(ICC ranging from 0.818 to 0.883) and a moderate correlation for the coronal and transverse parameters (ICC ranging from 0.520 to 0.608). The Passing Bablok linear regression analysis indicated that the confidence intervals for the intercepts of all parameters included 0, while the confidence intervals for the slopes of most parameters encompassed 1 except for step width, pelvic obliquity, and hip adduction-abduction angle. The implementation of Watrix system significantly decreased the testing duration for participants.

The Watrix system demonstrated relatively high test-retest reliability. The Watrix and BTS systems demonstrated moderate to good agreement for most parameters. However, the Watrix system tended to underestimate coronal and transverse plane parameters, resulting in lower consistency. In addition, the markerless motion capture system greatly reduces the testing duration.Optimizing algorithms to improve recognition accuracy remains the main direction of research.

Markerless vision-based human pose estimation (HPE) is a promising avenue towards scalable data collection in rehabilitation. Deploying this technology will require self-contained systems able to process data efficiently and accurately. The aims of this work are to (1) Determine how depth data affects lightweight monocular red-green-blue (RGB) HPE performance (accuracy and speed), to inform sensor selection and (2) Validate HPE models using data from individuals with physical impairments.

Two HPE models were investigated: Dite-HRNet and MobileHumanPose (capable of 2D and 3D HPE, respectively). The models were modified to include depth data as an input using three different fusion techniques: an early fusion method, a simple intermediate fusion method (using concatenation), and a complex intermediate fusion method (using specific fusion blocks, additional convolutional layers, and concatenation). All fusion techniques used RGB-D data, in contrast to the original models which only used RGB data. The models were trained, validated and tested using the CMU Panoptic and Human3.6 M data sets as well as a custom data set. The custom data set includes RGB-D and optical motion capture data of 15 uninjured and 12 post-stroke individuals, while they performed movements involving their upper limbs. HPE model performances were monitored through accuracy and computational efficiency. Evaluation metrics include Mean per Joint Position Error (MPJPE), Floating Point Operations (FLOPs) and frame rates (frames per second).

The early fusion architecture consistently delivered the lowest MPJPE in both 2D and 3D HPE cases while achieving similar FLOPs and frame rates to its RGB counterpart. These results were consistent regardless of the data used for training and testing the HPE models. Comparisons between the uninjured and stroke groups did not reveal a significant effect (all p values > 0.36) of motor impairment on the accuracy of any model.

Including depth data using an early fusion architecture improves the accuracy-efficiency trade-off of the HPE model. HPE accuracy is not affected by the presence of physical impairments. These results suggest that using depth data with RGB data is beneficial to HPE, and that models trained with data collected from uninjured individuals can generalize to persons with physical impairments.

Gait disturbance is one of the most common symptoms in patients with Parkinson's disease (PD) that is closely associated with poor clinical outcomes. Recently, video-based human pose estimation (HPE) technology has attracted attention as a cheaper and simpler method for performing gait analysis than marker-based 3D motion capture systems. However, it remains unclear whether video-based HPE is a feasible method for measuring temporospatial and kinematic gait parameters in patients with PD and how this function varies with camera position. In this study, treadmill and overground walking in 24 patients with early PD was measured using a motion capture system and two smartphone cameras placed on the near-frontal and lateral sides of the subjects. We compared the differences in temporospatial gait parameters and kinematic characteristics between joint position data obtained from the 3D motion capture system and the markerless HPE. Our results confirm the feasibility of analyzing gait in patients with PD using HPE. Although the near-frontal view, where the heel and toe are clearly visible, is effective for estimating temporal gait parameters, the lateral view is particularly well-suited for assessing spatial gait parameters and joint angles. However, in clinical settings where lateral recordings are not feasible, near-frontal view recordings can still serve as a practical alternative to motion capture systems.

Gait analysis is a vital tool in the assessment of human movement and has been widely used in clinical settings to identify potential abnormalities in individuals. However, there is a lack of consensus on the normative values for gait metrics in large populations. The primary objective of this study is to establish a normative database of spatiotemporal gait metrics across various age groups, contributing to a broader understanding of human gait dynamics. By doing so, we aim to enhance the clinical utility of gait analysis in diagnosing and managing health conditions.

We conducted an observational case-control study involving 313 healthy participants. The MetaMotionC IMU by Mbientlab Inc., equipped with a triaxial accelerometer, gyroscope, and magnetometer, was used to capture gait data. The IMU was placed at the sternal angle of each participant to ensure optimal data capture during a 50 m walk along a flat, unobstructed pathway. Data were collected through a Bluetooth connection to a smartphone running a custom-developed application and subsequently analysed using IMUGaitPY, a specialised version of the GaitPY Python package.

The data showed that gait speeds decrease with ageing for males and females. The fastest gait speed is observed in the 41-50 age group at 1.35 ± 0.23 m/s. Males consistently exhibit faster gait speeds than females across all age groups. Step length and cadence do not have clear trends with ageing. Gait speed and step length increase consistently with height, with the tallest group (191-200 cm) walking at an average speed of 1.49 ± 0.12 m/s, with an average step length of 0.91 ± 0.05 m. Cadence, however, decreases with increasing height, with the tallest group taking 103.52 ± 5.04 steps/min on average.

This study has established a comprehensive normative database for the spatiotemporal gait metrics of gait speed, step length, and cadence, highlighting the complexities of gait dynamics across age and sex groups and the influence of height. Our findings offer valuable reference points for clinicians to distinguish between healthy and pathological gait patterns, facilitating early detection and intervention for gait-related disorders. Moreover, this database enhances the clinical utility of gait analysis, supporting more objective diagnoses and assessments of therapeutic interventions. The normative database provides a valuable reference future research and clinical practice. It enables a more nuanced understanding of how gait evolves with age, gender, and physical stature, thus informing the development of targeted interventions to maintain mobility and prevent falls in older adults. Despite potential selection bias and the cross-sectional nature of the study, the insights gained provide a solid foundation for further longitudinal studies and diverse sampling to validate and expand upon these findings.

Gait analysis is crucial for identifying functional deviations from the normal gait cycle and is essential for the individualized treatment of motor disorders such as cerebral palsy (CP). The primary contribution of this study is the introduction of a multimodal fuzzy logic system-based gait index (FLS-GIS), designed to provide numerical scores for gait patterns in both healthy children and those with CP, before and after surgery. This study examines and evaluates the surgical outcomes in children with CP who have undergone Achilles tendon lengthening. The FLS-GIS utilizes hierarchical feature fusion and fuzzy logic models to systematically evaluate and score gait patterns, focusing on spatial and temporal features across the hip, knee, and ankle joints. The two FLS types-1 (FLS-GIS-T1) and type-2 (FLS-GIS-T2) indices, respectively, were implemented to comprehensively study gait profiles. Starting with the gait parameters of all subjects, the changes in gait parameters in post-surgery children reflect significant improvements in gait dynamics, bringing walking patterns in CP children closer to those of their typically healthy peers. Both FLS-GIS-T1 and FLS-GIS-T2 demonstrated significant improvements in post-surgery evaluations compared to pre-surgery assessments, with p values < 0.05 and < 0.001, respectively, when compared to traditional indices. The proposed FLS-based index offers clinicians a robust and standardized gait evaluation tool, characterized by a fixed range of values, enabling consistent assessment across various gait conditions.

Background/Objectives: Gait analysis, traditionally performed with lab-based optical motion capture systems, offers high accuracy but is costly and impractical for real-world use. Wearable technologies, especially inertial measurement units (IMUs), enable portable and accessible assessments outside the lab, though challenges with sensor placement, signal selection, and algorithm design can affect accuracy. This systematic review aims to bridge the benchmarking gap between IMU-based and traditional systems, validating the use of wearable inertial systems for gait analysis. Methods: This review examined English studies between 2012 and 2023, retrieved from the Scopus database, comparing wearable sensors to optical motion capture systems, focusing on IMU body placement, gait parameters, and validation metrics. Exclusion criteria for the search included conference papers, reviews, unavailable papers, studies without wearable inertial sensors for gait analysis, and those not involving agreement studies or optical motion capture systems. Results: From an initial pool of 479 articles, 32 were selected for full-text screening. Among them, the lower body resulted in the most common site for single IMU placement (in 22 studies), while the most frequently used multi-sensor configuration involved IMU positioning on the lower back, shanks, feet, and thighs (10 studies). Regarding gait parameters, 11 studies out of the 32 included studies focused on spatial-temporal parameters, 12 on joint kinematics, 2 on gait events, and the remainder on a combination of parameters. In terms of validation metrics, 24 studies employed correlation coefficients as the primary measure, while 7 studies used a combination of error metrics, correlation coefficients, and Bland-Altman analysis. Validation metrics revealed that IMUs exhibited good to moderate agreement with optical motion capture systems for kinematic measures. In contrast, spatiotemporal parameters demonstrated greater variability, with agreement ranging from moderate to poor. Conclusions: This review highlighted the transformative potential of wearable IMUs in advancing gait analysis beyond the constraints of traditional laboratory-based systems.

Clinical gait analysis plays a pivotal role in diagnosing and treating walking impairments. Inertial measurement units (IMUs) offer a low-cost, portable, and practical alternative to traditional gait analysis equipment, making these techniques more accessible beyond specialized clinics. Previous work and algorithms developed for specific clinical populations, like in individuals with Parkinson's disease, often do not translate effectively to other groups, such as stroke survivors, who exhibit significant variability in their gait patterns. The Salarian gait segmentation algorithm (SGSA) has demonstrated the potential to detect gait events and subsequently estimate clinical measures of gait speed, stride time, and other temporal parameters using two leg-worn IMUs in individuals with Parkinson's disease. However, the distinct gait impairments in stroke survivors, including hemiparesis, spasticity, and muscle weakness, can interfere with SGSA performance. Thus, the objective of this study was to develop and test an enhanced gait segmentation algorithm (EGSA) to capture temporal gait parameters in individuals with stroke.

Forty-one individuals with stroke were recruited from two acute rehabilitation settings and completed brief walking bouts with two leg-worn IMUs. We compared foot-off (FO), foot contact (FC), and temporal gait parameters computed from the SGSA and EGSA against ground truth measurements from an instrumented mat.

The EGSA demonstrated greater accuracy than the SGSA when detecting gait events within one second, for both FO (96% vs. 90%) and FC (94% vs. 91%). The EGSA also demonstrated lower error than the SGSA when detecting paretic FC, and FO events in slow, asymmetrical, and non-paretic footfalls. Temporal gait parameters from the EGSA had high reliability (ICC > 0.90) for stride time, step time, stance time, and double support time across gait speeds and levels of asymmetry.

This approach has the potential to enhance the accuracy and validity of IMU-based gait analysis in individuals with stroke, thereby enhancing clinicians' ability to monitor and intervene for gait impairments in a rehabilitation setting and beyond.

Clinical gait analysis plays a central role in the rehabilitation of stroke patients. However, practical and technical challenges limit their use in clinical settings. This study aimed to validate SMARTGAIT, a deep learning-based gait analysis system that addresses these limitations. Eight stroke patients took part in the study at the Human Performance Research Centre of the University of Konstanz. Gait measurements were taken using both the marker-based Vicon motion capture system and the single-smartphone-based SMARTGAIT system. We evaluated the agreement for knee, hip, and ankle joint angle kinematics in the frontal and sagittal plane and spatiotemporal gait parameters between the two systems. The results mostly demonstrated high levels of agreement between the two systems, with Pearson correlations of ≥0.79 for all lower body angle kinematics in the sagittal plane and correlations of ≥0.71 in the frontal plane. RMSE values were ≤4.6°. The intraclass correlation coefficients for all derived gait parameters showed good to excellent levels of agreement. SMARTGAIT is a promising tool for gait analysis in stroke, particularly for quantifying gait characteristics in the sagittal plane, which is very relevant for clinical gait analysis. However, further analyses are required to validate the use of SMARTGAIT in larger samples and its transferability to different types of pathological gait. In conclusion, a single smartphone recording (monocular 2D RGB camera) could make gait analysis more accessible in clinical settings, potentially simplifying the process and making it more feasible for therapists and doctors to use in their day-to-day practice.

Sensori-/psychomotor abnormalities refer to a wide range of disturbances in individual motor, affective and behavioral functions that are often observed in mental disorders. However, many of these studies have mainly used clinical rating scales, which can be potentially confounded by observer bias and are not able to detect subtle sensori-/psychomotor abnormalities. Yet, an innovative three-dimensional (3D) optical motion capturing technology (MoCap) can provide more objective and quantifiable data about movements and posture in psychiatric patients. To draw attention to recent rapid progress in the field, we performed a systematic review using PubMed, Medline, Embase, and Web of Science until May 01st 2024. We included 55 studies in the qualitative analysis and gait was the most examined movement. The identified studies suggested that sensori-/psychomotor abnormalities in neurodevelopmental, mood, schizophrenia spectrum and neurocognitive disorders are associated with alterations in spatiotemporal parameters (speed, step width, length and height; stance time, swing time, double limb support time, phases duration, adjusting sway, acceleration, etc.) during various movements such as walking, running, upper body, hand and head movements. Some studies highlighted the advantages of 3D optical MoCap systems over traditional rating scales and measurements such as actigraphy and ultrasound gait analyses. 3D optical MoCap systems are susceptible to detecting differences not only between patients with mental disorders and healthy persons but also among at-risk individuals exhibiting subtle sensori-/psychomotor abnormalities. Overall, 3D optical MoCap systems hold promise for objectively examining sensori-/psychomotor abnormalities, making them valuable tools for use in future clinical trials.

Background/Objectives: Parkinson's Disease is a prevalent neurodegenerative disorder affecting millions worldwide, primarily marked by motor and non-motor symptoms due to the degeneration of dopamine-producing neurons. Despite the absence of a cure, current treatments focus on symptom management, often relying on pharmacotherapy and surgical interventions. Early diagnosis remains a critical challenge, particularly in underserved areas, as existing diagnostic protocols lack standardization and accessibility. This paper proposes a novel framework for the diagnosis and severity classification of PD using video data captured in uncontrolled environments. Methods: Leveraging deep learning techniques, our approach synthesizes Skeleton Energy Images (SEIs) from gait sequences and employs three advanced models-a Convolutional Neural Network (CNN), a Residual Network (ResNet), and a Vision Transformer (ViT)-to analyze these images. Our methodology allows for the accurate detection of PD and differentiation of its severity without requiring specialized equipment or professional oversight. The dataset used consists of labeled videos capturing the early stages of the disease, facilitating the potential for timely intervention. Results: The four models performed very accurately during the training phase. In fact, an accuracy higher than 99% was achieved by the ViT and ResNet models. Moreover, a lesser accuracy of 90% was achieved by the CNN five-layer model. During the test phase, only the best-performing models from the training experiments were tested. The ResNet-18 model has achieved a 100% accuracy. However, the ViT and the CNN five-layer models have achieved, respectively, 99.96% and 96.40% test accuracy. Conclusions: The results demonstrate high accuracy, highlighting the framework's capabilities, and in particular the effectiveness of the workflow used for generating the SEI images. Given the nature of the dataset used, the proposed framework stands to function as a cost-effective and accessible tool for early PD detection in various healthcare settings. This study contributes to the advancement of mobile health technologies, aiming to enhance early diagnosis and monitoring of Parkinson's Disease.

Walking ability is essential for maintaining functional independence, but it can be impaired by conditions like hemiplegia resulting from a stroke event. In post-stroke populations, accurately assessing gait anomalies is crucial for rehabilitation to promote functional recovery, and to prevent falls or injuries.

The aim of this study is to evaluate gait-related parameters using a solution based on a single RGB-D camera, specifically Microsoft Azure Kinect DK (MAK), on a short walkway in both healthy (n= 27) and post-stroke individuals with hemiplegia (n= 20). The spatio-temporal and center of mass (CoM) parameters estimated by this approach were compared with those obtained from a gold standard motion capture (MoCap) system for instrumented 3D gait analysis.

The overall findings demonstrated high levels of accuracy (> 93%), and strong correlations (r > 0.9) between the parameters estimated by the two systems for both healthy and hemiplegic gait. In particular, some spatio-temporal parameters showed excellent agreement in both groups, while CoM displacements exhibited slightly lower correlation values in healthy individuals.

The results of the study suggest that a solution based on a single optical sensor could serve as an effective intermediate tool for gait analysis, not only in clinical settings or controlled environments but also in those contexts where gold standard systems are not feasible.

Analyze whether there are immediate changes in peak soleus activation and peak hindfoot eversion after short-foot exercise (SFE) in runners with medial tibial stress syndrome (MTSS). Secondarily, establish differences in peak soleus activation and peak hindfoot eversion between asymptomatic individuals and those presenting MTSS.

Quasi-experimental study.

University Laboratory.

Thirty-two runners participated: 16 with MTSS and 16 in the no-pain group (NPG).

Soleus activation was measured using electromyography, and hindfoot eversion via 3D kinematic analysis. Participants performed SFE, and running data were collected at 9,12 and 15 km/h pre- and post-intervention.

SFE reduced peak soleus activation at 9 (p = 0.017) and 15 km/h (p = 0.019) for the MTSS group and at 15 km/h (p < 0.001) for the NPG, suggesting improved neuromuscular efficiency and potentially reduced tibial stress. SFE did not significantly affect peak hindfoot eversion. Significant correlations were found between ankle dorsiflexion range of motion and muscle activation (r = 0.585 to 0.849, p < 0.05). These findings suggest SFE could improve neuromuscular efficiency and reduce tibial stress, and highlights ankle flexibility's role in muscle activation.

SFE significantly reduces soleus activation, potentially improving neuromuscular efficiency and decreasing tibial stress.

Human motion is often tracked using three-dimensional video motion tracking systems, which have demonstrated high levels of validity. More recently, inertial measurement units (IMUs) have been used to measure human movement due to their ease of access and application. The purpose of this study was to systematically review the literature regarding the validity of inertial sensor systems when being used to track human motion. Four electronic databases were used for the search, and eleven studies were included in the final review. IMUs have a high level of agreement with motion capture systems in the frontal and sagittal planes, measured with root mean square error (RMSE), intraclass correlation coefficient, and Pearson's correlation. However, the transverse or rotational planes began to show large discrepancies in joint angles between systems. Furthermore, as the intensity of the task being measured increased, the RMSE values began to get much larger. Currently, the use of accelerometers and inertial sensor systems has limited application in the assessment of human motion, but if the precision and processing of IMU devices improves further, it could provide researchers an opportunity to collect data in less synthetic environments, as well as improve ease of access to biomechanically analyse human movement.

Foot and ankle disorders are a very common diseases, represent a risk factor for falls in older people, and are associated with difficulty performing activities of daily living. With an increasing demand for cost-effective and high-quality clinical services, wearable technology can be strategic in extending our reach to patients with foot and ankle disorders. In recent years, wearable sensors have been increasingly utilized to assess the clinical outcomes of surgery, rehabilitation, and orthotic treatments. This article highlights recent achievements and developments in wearable sensor-based foot and ankle clinical assessment. An increasing number of studies have established the feasibility and effectiveness of wearable technology tools for foot and ankle disorders. Different methods and outcomes for feasibility studies have been introduced, such as satisfaction and efficacy in rehabilitation, surgical, and orthotic treatments. Currently, the widespread application of wearable sensors in clinical fields is hindered by a lack of robust evidence; in fact, only a few tests and analysis protocols are validated with cut-off values reported in the literature. However, nowadays, these tools are useful in quantifying clinical results before and after clinical treatments, providing useful data, also collected in real-life conditions, on the results of therapies.

Wearable gait analysis systems using inertial sensors offer the potential for easy-to-use gait assessment in lab and free-living environments. This can enable objective long-term monitoring and decision making for individuals with gait disabilities. This study explores a novel approach that applies a hidden Markov model-based similarity measure (HMM-SM) to assess changes in gait patterns based on the gyroscope and accelerometer signals from just one or two inertial sensors. Eleven able-bodied individuals were equipped with a system which perturbed gait patterns by manipulating stance-time symmetry. Inertial sensor data were collected from various locations on the lower body to train hidden Markov models. The HMM-SM was evaluated to determine whether it corresponded to changes in gait as individuals deviated from their baseline, and whether it could provide a reliable measure of gait similarity. The HMM-SM showed consistent changes in accordance with stance-time symmetry in the following sensor configurations: pelvis, combined upper leg signals, and combined lower leg signals. Additionally, the HMM-SM demonstrated good reliability for the combined upper leg signals (ICC = 0.803) and lower leg signals (ICC = 0.795). These findings provide preliminary evidence that the HMM-SM could be useful in assessing changes in overall gait patterns. This could enable the development of compact, wearable systems for unsupervised gait assessment, without the requirement to pre-identify and measure a set of gait parameters.

This research aimed to develop an algorithm for classifying scalogram images generated from electromyography data of patients with Rheumatoid Arthritis and Prolapsed Intervertebral Disc. Electromyography is valuable for assessing muscle function and diagnosing neurological disorders, but limitations, such as background noise, cross-talk, and inter-subject variability complicate the interpretation and assessment. To mitigate this, the present study uses scalogram images and attention-network architecture. The algorithm utilises a combination of features extracted from an attention module and a convolution feature module, followed by classification using a Convolutional Neural Network classifier. A comparison of eight alternative architectures, including individual implementations of attention and convolution filters and a Convolutional Neural Network-only model, shows that the hybrid Convolutional Neural Network model proposed in this study outperforms the others. The model exhibits excellent discriminatory ability between gait abnormalities with an accuracy of 96.7%, a precision of 95.2%, a recall of 94.8%, and an Area Under Curve of 0.99. These findings suggest that the proposed model is highly accurate in classifying scalogram images of electromyography signals and may have significant clinical implications for early diagnosis and treatment planning.

In recent years, radar technology has been extensively utilized in contactless human behavior monitoring systems. The unique capabilities of ultra-wideband (UWB) radars compared to conventional radar technologies, due to time-of-flight measurements, present new untapped opportunities for in-depth monitoring of human movement during overground locomotion. This study aims to investigate the deployability of UWB radars in accurately capturing the gait patterns of healthy individuals with no known walking impairments.

A novel algorithm was developed that can extract ten clinical spatiotemporal gait features using the Doppler information captured from three monostatic UWB radar sensors during a 6-meter walking task. Key gait events are detected from lower-extremity movements based on the joint range-Doppler-time representation of recorded radar data. The estimated gait parameters were validated against a gold-standard optical motion tracking system using 12 healthy volunteers.

On average, nine gait parameters can be consistently estimated with 90-98% accuracy, while capturing 94.5% of participants' gait variability and 90.8% of inter-limb symmetry. Correlation and Bland-Altman analysis revealed a strong correlation between radar-based parameters and the ground-truth values, with average discrepancies consistently close to 0.

Results prove that radar sensing can provide accurate biomarkers to supplement clinical human gait analysis, with quality similar to gold standard assessment.

Radars can potentially allow a transition from expensive and cumbersome lab-based gait analysis tools toward a completely unobtrusive and affordable solution for in-home deployment, enabling continuous long-term monitoring of individuals for research and healthcare applications.

Three-dimensional motion analysis using optoelectronic cameras and force platforms is typically used to quantify gait disorders. However, these systems have various limitations, particularly when assessing patients in an ecological environment. To address these limitations, several wearable devices have been developed. However, few studies have reported metrological information regarding their repeatability and sensitivity to change.

A healthy adult performed 6 min walking tests with FeetMe® system insoles under different walking conditions overground and on a treadmill. The standard error of measurement (SEM), the minimum detectable differences (MDDs), and the effect size (ES) were calculated for spatio-temporal parameters, and the ground reaction force was calculated from the 16,000 steps recorded.

SEM values were below 3.9% for the ground reaction force and below 6.8% for spatio-temporal parameters. ES values were predominantly high, with 72.9% of cases between overground and treadmill conditions with induced asymmetry, and 64.5% of cases between treadmill conditions with and without induced asymmetry exhibiting an ES greater than 1.2. The minimum detectable differences ranged from 4.5% to 10.7% for ground reaction forces and 2.1% to 18.9% for spatio-temporal parameters.

Our study demonstrated that the FeetMe® system is a reliable solution. The sensitivity to change showed that these instrumented insoles can effectively reflect patient asymmetry and progress.

Parkinson's disease (PD) is the second most common movement disorder in the world. It is characterized by motor and non-motor symptoms that have a profound impact on the independence and quality of life of people affected by the disease, which increases caregivers' burdens. The use of the quantitative gait data of people with PD and deep learning (DL) approaches based on gait are emerging as increasingly promising methods to support and aid clinical decision making, with the aim of providing a quantitative and objective diagnosis, as well as an additional tool for disease monitoring. This will allow for the early detection of the disease, assessment of progression, and implementation of therapeutic interventions. In this paper, the authors provide a systematic review of emerging DL techniques recently proposed for the analysis of PD by using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Scopus, PubMed, and Web of Science databases were searched across an interval of six years (between 2018, when the first article was published, and 2023). A total of 25 articles were included in this review, which reports studies on the movement analysis of PD patients using both wearable and non-wearable sensors. Additionally, these studies employed DL networks for classification, diagnosis, and monitoring purposes. The authors demonstrate that there is a wide employment in the field of PD of convolutional neural networks for analyzing signals from wearable sensors and pose estimation networks for motion analysis from videos. In addition, the authors discuss current difficulties and highlight future solutions for PD monitoring and disease progression.

Gait abnormality detection is a challenging task in clinical practice. The majority of the current frameworks for gait abnormality detection involve the individual processes of segmentation, feature estimation, feature learning, and similarity assessment. Since each component of these modules is fixed and they are mutually independent, their performance under difficult circumstances is not ideal. We combine those processes into a single framework, a gait abnormality detection system with an end-to-end network.

It is made up of convolutional neural networks and Deep-Q-learning methods: one for coordinate estimation and the other for classification. In a single joint learning technique that may be trained together, the two networks are modeled. This method is significantly more efficient for use in real life since it drastically simplifies the conventional step-by-step approach.

The proposed model is experimented on MATLAB R2020a. While considering into consideration the stability factor, our proposed model attained an average case accuracy of 95.3%, a sensitivity of 96.4%, and a specificity of 94.1%.

Our paradigm for quantifying gait analysis using commodity equipment will improve access to quantitative gait analysis in medical facilities and rehabilitation centers while also allowing academics to conduct large-scale investigations for gait-related disorders. Numerous experimental findings demonstrate the effectiveness of the proposed strategy and its ability to provide cutting-edge outcomes.

Depression is currently a major contributor to unnatural deaths and the healthcare burden globally, and a patient's battle with depression is often a long one. Because the causes, symptoms, and effects of medications are complex and highly individualized, early identification and personalized treatment of depression are key to improving treatment outcomes. The development of wearable electronics, machine learning, and other technologies in recent years has provided more possibilities for the realization of this goal. Conducting regular monitoring through biosensing technology allows for a more comprehensive and objective analysis than previous self-evaluations. This includes identifying depressive episodes, distinguishing somatization symptoms, analyzing etiology, and evaluating the effectiveness of treatment programs. This review summarizes recent research on biosensing technologies for depression. Special attention is given to technologies that can be portable or wearable, with the potential to enable patient use outside of the hospital, for long periods.

Traditional methods for assessing movement quality rely on subjective standardized scales and clinical expertise. This limitation creates challenges for assessing patients with spinocerebellar ataxia (SCA), in whom changes in mobility can be subtle and varied. We hypothesized that a machine learning analytic system might complement traditional clinician-rated measures of gait. Our objective was to use a video-based assessment of gait dispersion to compare the effects of troriluzole with placebo on gait quality in adults with SCA.

Participants with SCA underwent gait assessment in a phase 3, double-blind, placebo-controlled trial of troriluzole (NCT03701399). Videos were processed through a deep learning pose extraction algorithm, followed by the estimation of a novel gait stability measure, the Pose Dispersion Index, quantifying the frame-by-frame symmetry, balance, and stability during natural and tandem walk tasks. The effects of troriluzole treatment were assessed in mixed linear models, participant-level grouping, and treatment group-by-visit week interaction adjusted for age, sex, baseline modified Functional Scale for the Assessment and Rating of Ataxia (f-SARA), and time since diagnosis.

From 218 randomized participants, 67 and 56 participants had interpretable videos of a tandem and natural walk attempt, respectively. At Week 48, individuals assigned to troriluzole exhibited significant (p = 0.010) improvement in tandem walk Pose Dispersion Index versus placebo {adjusted interaction coefficient: 0.584 [95% confidence interval (CI) 0.137 to 1.031]}. A similar, nonsignificant trend was observed in the natural walk assessment [coefficient: 1.198 (95% CI - 1.067 to 3.462)]. Further, lower baseline Pose Dispersion Index during the natural walk was significantly (p = 0.041) associated with a higher risk of subsequent falls [adjusted Poisson coefficient: - 0.356 [95% CI - 0.697 to - 0.014)].

Using this novel approach, troriluzole-treated subjects demonstrated improvement in gait as compared to placebo for the tandem walk. Machine learning applied to video-captured gait parameters can complement clinician-reported motor assessment in adults with SCA. The Pose Dispersion Index may enhance assessment in future research. TRIAL REGISTRATION-CLINICALTRIALS.

NCT03701399.

Conventional patient-reported surveys, used for patients undergoing total hip arthroplasty (THA), are limited by subjectivity and recall bias. Objective functional evaluation, such as gait analysis, to delineate a patient's functional capacity and customize surgical interventions, may address these shortcomings. This systematic review endeavours to investigate the application of objective functional assessments in appraising individuals undergoing THA.

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were applied. Eligible studies of THA patients that conducted at least one type of objective functional assessment both pre- and postoperatively were identified through Embase, Medline/PubMed, and Cochrane Central database-searching from inception to 15 September 2023. The assessments included were subgrouped for analysis: gait analysis, motion analysis, wearables, and strength tests.

A total of 130 studies using 15 distinct objective functional assessment methods (FAMs) were identified. The most frequently used method was instrumented gait/motion analysis, followed by the Timed-Up-and-Go test (TUG), 6 minute walk test, timed stair climbing test, and various strength tests. These assessments were characterized by their diagnostic precision and applicability to daily activities. Wearables were frequently used, offering cost-effectiveness and remote monitoring benefits. However, their accuracy and potential discomfort for patients must be considered.

The integration of objective functional assessments in THA presents promise as a progress-tracking modality for improving patient outcomes. Gait analysis and the TUG, along with advancing wearable sensor technology, have the potential to enhance patient care, surgical planning, and rehabilitation.

Gait monitoring using hip joint angles offers a promising approach for person identification, leveraging the capabilities of smartphone inertial measurement units (IMUs). This study investigates the use of smartphone IMUs to extract hip joint angles for distinguishing individuals based on their gait patterns. The data were collected from 10 healthy subjects (8 males, 2 females) walking on a treadmill at 4 km/h for 10 min. A sensor fusion technique that combined accelerometer, gyroscope, and magnetometer data was used to derive meaningful hip joint angles. We employed various machine learning algorithms within the WEKA environment to classify subjects based on their hip joint pattern and achieved a classification accuracy of 88.9%. Our findings demonstrate the feasibility of using hip joint angles for person identification, providing a baseline for future research in gait analysis for biometric applications. This work underscores the potential of smartphone-based gait analysis in personal identification systems.

This paper proposes a scheme for predicting ground reaction force (GRF) and center of pressure (CoP) using low-cost FSR sensors. GRF and CoP data are commonly collected from smart insoles to analyze the wearer's gait and diagnose balance issues. This approach can be utilized to improve a user's rehabilitation process and enable customized treatment plans for patients with specific diseases, making it a useful technology in many fields. However, the conventional measuring equipment for directly monitoring GRF and CoP values, such as F-Scan, is expensive, posing a challenge to commercialization in the industry. To solve this problem, this paper proposes a technology to predict relevant indicators using only low-cost Force Sensing Resistor (FSR) sensors instead of expensive equipment. In this study, data were collected from subjects simultaneously wearing a low-cost FSR Sensor and an F-Scan device, and the relationship between the collected data sets was analyzed using supervised learning techniques. Using the proposed technique, an artificial neural network was constructed that can derive a predicted value close to the actual F-Scan values using only the data from the FSR Sensor. In this process, GRF and CoP were calculated using six virtual forces instead of the pressure value of the entire sole. It was verified through various simulations that it is possible to achieve an improved prediction accuracy of more than 30% when using the proposed technique compared to conventional prediction techniques.

(1) Background: Traditional gait assessment methods have limitations like time-consuming procedures, the requirement of skilled personnel, soft tissue artifacts, and high costs. Various 3D time scanning techniques are emerging to overcome these issues. This study compares a 3D temporal scanning system (Move4D) with an inertial motion capture system (Xsens) to evaluate their reliability and accuracy in assessing gait spatiotemporal parameters and joint kinematics. (2) Methods: This study included 13 healthy people and one hemiplegic patient, and it examined stance time, swing time, cycle time, and stride length. Statistical analysis included paired samples t-test, Bland-Altman plot, and the intraclass correlation coefficient (ICC). (3) Results: A high degree of agreement and no significant difference (p > 0.05) between the two measurement systems have been found for stance time, swing time, and cycle time. Evaluation of stride length shows a significant difference (p < 0.05) between Xsens and Move4D. The highest root-mean-square error (RMSE) was found in hip flexion/extension (RMSE = 10.99°); (4) Conclusions: The present work demonstrated that the system Move4D can estimate gait spatiotemporal parameters (gait phases duration and cycle time) and joint angles with reliability and accuracy comparable to Xsens. This study allows further innovative research using 4D (3D over time) scanning for quantitative gait assessment in clinical practice.

Developments in vision-based systems and human pose estimation algorithms have the potential to detect, monitor and intervene early on neurodegenerative diseases through gait analysis. However, the gap between the technology available and actual clinical practice is evident as most clinicians still rely on subjective observational gait analysis or objective marker-based analysis that is time-consuming.

This paper aims to examine the main developments of vision-based motion capture and how such advances may be integrated into clinical practice.

The literature review was conducted in six online databases using Boolean search terms. A commercial system search was also included. A predetermined methodological criterion was then used to assess the quality of the selected articles.

A total of seventeen studies were evaluated, with thirteen studies focusing on gait classification systems and four studies on gait measurement systems. Of the gait classification systems, nine studies utilized artificial intelligence-assisted techniques, while four studies employed statistical techniques. The results revealed high correlations of gait features identified by classifier models with existing clinical rating scales. These systems demonstrated generally high classification accuracies and were effective in diagnosing disease severity levels. Gait measurement systems that extract spatiotemporal and kinematic joint information from video data generally found accurate measurements of gait parameters with low mean absolute errors, high intra- and inter-rater reliability.

Low cost, portable vision-based systems can provide proof of concept for the quantification of gait, expansion of gait assessment tools, remote gait analysis of neurodegenerative diseases and a point of care system for orthotic evaluation. However, certain challenges, including small sample sizes, occlusion risks, and selection bias in training models, need to be addressed. Nevertheless, these systems can serve as complementary tools, equipping clinicians with essential gait information to objectively assess disease severity and tailor personalized treatment for enhanced patient care.

Quantitative 3D movement analysis using inertial measurement units (IMUs) allows for a more detailed characterization of motor patterns than clinical assessment alone. It is essential to discriminate between gait features that are responsive or unresponsive to current therapies to better understand the underlying pathophysiological basis and identify potential therapeutic strategies.

This study aims to characterize the responsiveness and temporal evolution of different gait subcomponents in Parkinson's disease (PD) patients in their OFF and various ON states following levodopa administration, utilizing both wearable sensors and the gold-standard MDS-UPDRS motor part III.

Seventeen PD patients were assessed while wearing a full-body set of 15 IMUs in their OFF state and at 20-minute intervals following the administration of a supra-threshold levodopa dose. Gait was reconstructed using a biomechanical model of the human body to quantify how each feature was modulated. Comparisons with non-PD control subjects were conducted in parallel.

Significant motor changes were observed in both the upper and lower limbs according to the MDS-UPDRS III, 40 minutes after levodopa intake. IMU-assisted 3D kinematics detected significant motor alterations as early as 20 minutes after levodopa administration, particularly in upper limbs metrics. Although all "pace-domain" gait features showed significant improvement in the Best-ON state, most rhythmicity, asymmetry, and variability features did not.

IMUs are capable of detecting motor alterations earlier and in a more comprehensive manner than the MDS-UPDRS III. The upper limbs respond more rapidly to levodopa, possibly reflecting distinct thresholds to levodopa across striatal regions.

This review explores the evolutionary trajectory of navigation assistance tools tailored for the visually impaired, spanning from traditional aids like white canes to contemporary electronic devices. It underlines their pivotal role in fostering safe mobility for visually impaired individuals.

The primary aim is to categorize and assess the plethora of navigation assistance solutions available. Emphasis is placed on technological advancements, particularly in electronic systems employing sensors, AI, and feedback mechanisms. Furthermore, the review underscores the emerging influence of smartphone-based solutions and navigation satellite systems in augmenting independence and quality of life for the visually impaired.

Navigation assistance solutions are segmented into four key categories: Visual Imagery Systems, Non-Visual Data Systems, Map-Based Solutions, and 3D Sound Systems. The integration of diverse sensors like Ultrasonic Sensors and LiDAR for obstacle detection and real-time feedback is scrutinized. Additionally, the fusion of smartphone technology with sensors to deliver location-based assistance is explored. The review also evaluates the functionality, efficacy, and cost-efficiency of navigation satellite systems.

Results indicate a significant evolution in navigation aids, with modern electronic systems proving highly effective in aiding obstacle detection and safe navigation. The convenience and portability of smartphone-based solutions are underscored, along with the potential of navigation satellite systems to enhance navigation assistance.

In conclusion, the review advocates for continued innovation and technological integration in navigation tools to empower visually impaired individuals with increased independence and safe access to their surroundings. It accentuates the imperative of ongoing efforts to enhance the quality of life for those with visual impairments through futuristic technological solutions.

An easy-to-use and reliable tool is essential for gait assessment of people with gait pathologies. This study aimed to assess the reliability and validity of the OneStep smartphone application compared to the C-Mill-VR+ treadmill (Motek, Nederlands), among patients undergoing rehabilitation for unilateral lower extremity disability. Spatiotemporal gait parameters were extracted from the treadmill and from two smartphones, one on each leg. Inter-device reliability was evaluated using Pearson correlation, intra-cluster correlation coefficient (ICC), and Cohen's d, comparing the application's readings from the two phones. Validity was assessed by comparing readings from each phone to the treadmill. Twenty-eight patients completed the study; the median age was 45.5 years, and 61% were males. The ICC between the phones showed a high correlation (r = 0.89-1) and good-to-excellent reliability (ICC range, 0.77-1) for all the gait parameters examined. The correlations between the phones and the treadmill were mostly above 0.8. The ICC between each phone and the treadmill demonstrated moderate-to-excellent validity for all the gait parameters (range, 0.58-1). Only 'step length of the impaired leg' showed poor-to-good validity (range, 0.37-0.84). Cohen's d effect size was small (d < 0.5) for all the parameters. The studied application demonstrated good reliability and validity for spatiotemporal gait assessment in patients with unilateral lower limb disability.

Approaches to gait analysis are evolving rapidly and now include a wide range of options: from e-patches to video platforms to wearable inertial measurement unit systems. Newer options for gait analysis are generally more inclusive for the assessment of children, more cost effective and easier to administer. However, there is limited data on the comparability of newer systems with more established traditional approaches in young children.

To determine comparability between the Physilog®5 wearable inertial sensor and GAITRite® electronic walkway for spatiotemporal (stride length, time and velocity, cadence) and relative phase (double support time, stance, swing, loading, foot flat and push off) data in young children.

A total 34 typically developing participants (41% female) aged 6-11 years old median age 8.99 years old (interquartile range 2.83) were assessed walking at self-selected speed over the GAITRite® electronic walkway while concurrently wearing shoe-attached Physilog®5 IMU sensors. Level of agreement was analysed by Lin's concordance correlation coefficient (CCC), Bland-Altman plots and 95% limit of agreement. Systematic bias was assessed using 95% confidence interval of the mean difference.

Excellent to almost perfect agreement was observed between systems for spatiotemporal metrics: cadence (CCC=0.996), stride length (CCC=0.993), stride time (CCC=0.996), stride velocity (CCC=0.988). The relative phase metrics adjusted for stride velocity showed improved comparability when compared to the unadjusted metrics: swing adjusted (adj) (CCC=0.635); stance adj (CCC: 0.879); loading adj: (CCC=0.626).

Spatiotemporal metrics are highly compatible across GAITRite® electronic walkway and Physilog®5 IMU systems in young children. Relative phase metrics were somewhat compatible between systems when adjusted for stride velocity.

Animals have been used as models to help to better understand biological and anatomical systems, and pathologies in both humans and non-human species, and sheep are often used as an in vivo experimental model for orthopedic research. Gait analysis has been shown to be an important tool in biomechanics research with clinical applications. The purpose of this study was to perform a kinematic analysis using a tridimensional (3D) reconstruction of the sheep hindlimb. Seven healthy sheep were evaluated for natural overground walking, and motion capture of the right hindlimb was collected with an optoelectronic system while the animals walked in a track. The analysis addressed gait spatiotemporal variables, hip, knee and ankle angle and intralimb joint angle coordination measures during the entire walking cycle. This study is the first that describes the spatiotemporal parameters from the hip, knee and ankle joints in a tridimensional way: flexion/extension; abduction/adduction and inter/external rotation. The results of this assessment can be used as an outcome indicator to guide treatment and the efficacy of different therapies for orthopedic and neurological conditions involving the locomotor system of the sheep animal model.

(1) Background: This study evaluates the effectiveness of low-frequency repetitive transcranial magnetic stimulation (LF-rTMS) in improving gait in post-stroke hemiplegic patients, using wearable sensor technology for objective gait analysis. (2) Methods: A total of 72 stroke patients were randomized into control, sham stimulation, and LF-rTMS groups, with all receiving standard medical treatment. The LF-rTMS group underwent stimulation on the unaffected hemisphere for 6 weeks. Key metrics including the Fugl-Meyer Assessment Lower Extremity (FMA-LE), Berg Balance Scale (BBS), Modified Barthel Index (MBI), and gait parameters were measured before and after treatment. (3) Results: The LF-rTMS group showed significant improvements in the FMA-LE, BBS, MBI, and various gait parameters compared to the control and sham groups (p < 0.05). Specifically, the FMA-LE scores improved by an average of 5 points (from 15 ± 3 to 20 ± 2), the BBS scores increased by 8 points (from 35 ± 5 to 43 ± 4), the MBI scores rose by 10 points (from 50 ± 8 to 60 ± 7), and notable enhancements in gait parameters were observed: the gait cycle time was reduced from 2.05 ± 0.51 s to 1.02 ± 0.11 s, the stride length increased from 0.56 ± 0.04 m to 0.97 ± 0.08 m, and the walking speed improved from 35.95 ± 7.14 cm/s to 75.03 ± 11.36 cm/s (all p < 0.001). No adverse events were reported. The control and sham groups exhibited improvements but were not as significant. (4) Conclusions: LF-rTMS on the unaffected hemisphere significantly enhances lower-limb function, balance, and daily living activities in subacute stroke patients, with the gait parameters showing a notable improvement. Wearable sensor technology proves effective in providing detailed, objective gait analysis, offering valuable insights for clinical applications in stroke rehabilitation.

Gait phase monitoring wearable sensors play a crucial role in assessing both health and athletic performance, offering valuable insights into an individual's gait pattern. In this study, we introduced a simple and cost-effective capacitive gait sensor manufacturing approach, utilizing a micropatterned polydimethylsiloxane dielectric layer placed between screen-printed silver electrodes. The sensor demonstrated inherent stretchability and durability, even when the electrode was bent at a 45-degree angle, it maintained an electrode resistance of approximately 3 Ω. This feature is particularly advantageous for gait monitoring applications. Furthermore, the fabricated flexible capacitive pressure sensor exhibited higher sensitivity and linearity at both low and high pressure and displayed very good stability. Notably, the sensors demonstrated rapid response and recovery times for both under low and high pressure. To further explore the capabilities of these new sensors, they were successfully tested as insole-type pressure sensors for real-time gait signal monitoring. The sensors displayed a well-balanced combination of sensitivity and response time, making them well-suited for gait analysis. Beyond gait analysis, the proposed sensor holds the potential for a wide range of applications within biomedical, sports, and commercial systems where soft and conformable sensors are preferred.

Peripheral neuropathy due to chemotherapeutic drugs causes alterations in ankle movement during gait. This study aimed to describe the spatiotemporal parameters and ankle kinematics during gait in schoolchildren with acute lymphoblastic leukemia with clinically suspected peripheral neuropathy.

In children with acute lymphoblastic leukemia in the maintenance phase, we calculated spatiotemporal and kinematic parameters of the ankle during gait using Kinovea® software. Furthermore, we identified alterations in the parameters obtained considering the values of the normality data from a stereophotogrammetry system as the reference values. Finally, we represented the kinematic parameters of the ankles calculated with Kinovea® compared to the normality values of the stereophotogrammetry.

We evaluated 25 schoolchildren; 13 were male (52.0%) with a median age of 88.0months and a median of 60.0 weeks in the maintenance phase, and 54.8% were classified as standard risk. Spatiotemporal parameters: cadence (steps/min), bilateral step length (m), and average gait speed (m/s) in ALL children were significantly lower than reference values (p < 0.001). Except for right mid-stance and bilateral foot strike, initial swing showed that both ankles maintained plantar flexion values during gait, significantly lower in ALL patients (p < 0.05).

We identified spatiotemporal and kinematics alterations in schoolchildren with acute lymphoblastic leukemia during all phases of the gait suggestive of alteration in ankle muscles during movement, probably due to peripheral neuropathy; nevertheless, our results should be taken with caution until the accuracy and reliability of Kinovea® software as a diagnostic test compared to the stereophotogrammetric system in children with ALL and healthy peers is proven.

Falls among the elderly are a major societal problem. While observations of medium-distance walking using inertial sensors identified potential fall predictors, classifying individuals at risk based on single gait cycles remains elusive. This challenge stems from individual variability and step-to-step fluctuations, making accurate classification difficult.

We recruited 44 participants, equally divided into high and low fall-risk groups. A smartphone secured on their second sacral spinous process recorded data during indoor walking. Features were extracted at each gait cycle from a 6-dimensional time series (tri-axial angular velocity and tri-axial acceleration) and classified using the gradient boosting decision tree algorithm.

Mean accuracy across five-fold cross-validation was 0.936. "Age" was the most influential individual feature, while features related to acceleration in the gait direction held the highest total relative importance when aggregated by axis (0.5365).

Combining acceleration, angular velocity data, and the gradient boosting decision tree algorithm enabled accurate fall risk classification in the elderly, previously challenging due to lack of discernible features. We reveal the first-ever identification of three-dimensional pelvic motion characteristics during single gait cycles in the high-risk group. This novel method, requiring only one gait cycle, is valuable for individuals with physical limitations hindering repetitive or long-distance walking or for use in spaces with limited walking areas. Additionally, utilizing readily available smartphones instead of dedicated equipment has potential to improve gait analysis accessibility.