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Special Issue "Sensor Technologies for Gait Analysis"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 10970

Special Issue Editor

Prof. Miguel Velhote Correia
E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Universidade do Porto, 4200-465 Porto, Portugal
Interests: sensors; electronics; biomedical instrumentation; computational vision; image and signal processing

Special Issue Information

Dear Colleagues,

The aim of this Special Issue is to highlight the most recent research regarding to sensor technologies for gait analysis. This Special Issue focuses on the development, validity, use, and applicability of devices in gait pattern identification, assessment and recognition. The broader aim is to collect high-quality papers from researchers around the world working in this area to make gait monitoring more widespread and more effective using sensor technologies. Research articles and reviews are solicited that provide a comprehensive insight into the sensor technologies for gait analysis on any aspect of novel sensor development and applications. Topics of interest include but are not limited to the following:

  • Gait Analysis
  • Gait Measurement
  • Gait Recognition
  • Impaired and modified gait analysis
  • Neurological gait disorders assessment
  • Machine Learning in Gait Analysis
  • Balance/Stability/Posture
  • Sports and Sports Performance
  • Muscles/electromyography
  • Rehabilitation
  • Novel Biomechanics
  • Data and Analysis Methods

Prof. Miguel Velhote Correia
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (13 papers)

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Article
A New Post-Processing Proposal for Improving Biometric Gait Recognition Using Wearable Devices
Sensors 2023, 23(3), 1054; https://doi.org/10.3390/s23031054 - 17 Jan 2023
Viewed by 192
Abstract
In this work, a novel Window Score Fusion post-processing technique for biometric gait recognition is proposed and successfully tested. We show that the use of this technique allows recognition rates to be greatly improved, independently of the configuration for the previous stages of [...] Read more.
In this work, a novel Window Score Fusion post-processing technique for biometric gait recognition is proposed and successfully tested. We show that the use of this technique allows recognition rates to be greatly improved, independently of the configuration for the previous stages of the system. For this, a strict biometric evaluation protocol has been followed, using a biometric database composed of data acquired from 38 subjects by means of a commercial smartwatch in two different sessions. A cross-session test (where training and testing data were acquired in different days) was performed. Following the state of the art, the proposal was tested with different configurations in the acquisition, pre-processing, feature extraction and classification stages, achieving improvements in all of the scenarios; improvements of 100% (0% error) were even reached in some cases. This shows the advantages of including the proposed technique, whatever the system. Full article
(This article belongs to the Special Issue Sensor Technologies for Gait Analysis)
Article
Multidimensional Biomechanics-Based Score to Assess Disease Progression in Duchenne Muscular Dystrophy
Sensors 2023, 23(2), 831; https://doi.org/10.3390/s23020831 - 11 Jan 2023
Viewed by 527
Abstract
(1) Background: Duchenne (DMD) is a rare neuromuscular disease that progressively weakens muscles, which severely impairs gait capacity. The Six Minute-Walk Test (6MWT), which is commonly used to evaluate and monitor the disease’s evolution, presents significant variability due to extrinsic factors such as [...] Read more.
(1) Background: Duchenne (DMD) is a rare neuromuscular disease that progressively weakens muscles, which severely impairs gait capacity. The Six Minute-Walk Test (6MWT), which is commonly used to evaluate and monitor the disease’s evolution, presents significant variability due to extrinsic factors such as patient motivation, fatigue, and learning effects. Therefore, there is a clear need for the establishment of precise clinical endpoints to measure patient mobility. (2) Methods: A novel score (6M+ and 2M+) is proposed, which is derived from the use of a new portable monitoring system capable of carrying out a complete gait analysis. The system includes several biomechanical sensors: a heart rate band, inertial measurement units, electromyography shorts, and plantar pressure insoles. The scores were obtained by processing the sensor signals and via gaussian-mixture clustering. (3) Results: The 6M+ and 2M+ scores were evaluated against the North Star Ambulatory Assessment (NSAA), the gold-standard for measuring DMD, and six- and two-minute distances. The 6M+ and 2M+ tests led to superior distances when tested against the NSAA. The 6M+ test and the 2M+ test in particular were the most correlated with age, suggesting that these scores better characterize the gait regressions in DMD. Additionally, the 2M+ test demonstrated an accuracy and stability similar to the 6M+ test. (4) Conclusions: The novel monitoring system described herein exhibited good usability with respect to functional testing in a clinical environment and demonstrated an improvement in the objectivity and reliability of monitoring the evolution of neuromuscular diseases. Full article
(This article belongs to the Special Issue Sensor Technologies for Gait Analysis)
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Article
AFOs Improve Stride Length and Gait Velocity but Not Motor Function for Most with Mild Cerebral Palsy
Sensors 2023, 23(2), 569; https://doi.org/10.3390/s23020569 - 04 Jan 2023
Viewed by 345
Abstract
Ankle–foot orthoses (AFOs) are prescribed to children with cerebral palsy (CP) in hopes of improving their gait and gross motor activities. The purpose of this retrospective study was to examine if clinically significant changes in gross motor function occur with the use of [...] Read more.
Ankle–foot orthoses (AFOs) are prescribed to children with cerebral palsy (CP) in hopes of improving their gait and gross motor activities. The purpose of this retrospective study was to examine if clinically significant changes in gross motor function occur with the use of AFOs in children and adolescents diagnosed with CP (Gross Motor Function Classification System levels I and II). Data from 124 clinical assessments were analyzed. Based on minimum clinically important difference (MCID), 77% of subjects demonstrated an increase in stride length, 45% of subjects demonstrated an increase in walking velocity, and 30% demonstrated a decrease in cadence. Additionally, 27% of the subjects demonstrated increase in gait deviation index (GDI). Deterioration in gait was evident by decreases in walking speed (5% of subjects), increases in cadence (11% of subjects), and 15% of subjects demonstrated decreases in gait deviation index. Twenty-two percent of subjects demonstrated no change in stride lengths and one participant demonstrated a decrease in stride length. However, AFOs improved Gross Motor Function Measure (GMFM) scores for a minority (10%) of children with mild CP (GMFCS level I and II), with 82–85% of subjects demonstrating no change in GMFM scores and 5–7% demonstrating decrease in GMFM scores. Full article
(This article belongs to the Special Issue Sensor Technologies for Gait Analysis)
Article
Predicting Vertical Ground Reaction Forces in Running from the Sound of Footsteps
Sensors 2022, 22(24), 9640; https://doi.org/10.3390/s22249640 - 08 Dec 2022
Viewed by 943
Abstract
From the point of view of measurement, footstep sounds represent a simple, wearable and inexpensive sensing opportunity to assess running biomechanical parameters. Therefore, the aim of this study was to investigate whether the sounds of footsteps can be used to predict the vertical [...] Read more.
From the point of view of measurement, footstep sounds represent a simple, wearable and inexpensive sensing opportunity to assess running biomechanical parameters. Therefore, the aim of this study was to investigate whether the sounds of footsteps can be used to predict the vertical ground reaction force profiles during running. Thirty-seven recreational runners performed overground running, and their sounds of footsteps were recorded from four microphones, while the vertical ground reaction force was recorded using a force plate. We generated nine different combinations of microphone data, ranging from individual recordings up to all four microphones combined. We trained machine learning models using these microphone combinations and predicted the ground reaction force profiles by a leave-one-out approach on the subject level. There were no significant differences in the prediction accuracy between the different microphone combinations (p < 0.05). Moreover, the machine learning model was able to predict the ground reaction force profiles with a mean Pearson correlation coefficient of 0.99 (range 0.79–0.999), mean relative root-mean-square error of 9.96% (range 2–23%) and mean accuracy to define rearfoot or forefoot strike of 77%. Our results demonstrate the feasibility of using the sounds of footsteps in combination with machine learning algorithms based on Fourier transforms to predict the ground reaction force curves. The results are encouraging in terms of the opportunity to create wearable technology to assess the ground reaction force profiles for runners in the interests of injury prevention and performance optimization. Full article
(This article belongs to the Special Issue Sensor Technologies for Gait Analysis)
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Article
Gait Recognition by Combining the Long-Short-Term Attention Network and Personal Physiological Features
Sensors 2022, 22(22), 8779; https://doi.org/10.3390/s22228779 - 14 Nov 2022
Viewed by 406
Abstract
Although gait recognition has been greatly improved by efforts from many researchers in recent years, its performance is still unsatisfactory due to the lack of gait information under the real scenariowhere only one or two images may be used for recognition. In this [...] Read more.
Although gait recognition has been greatly improved by efforts from many researchers in recent years, its performance is still unsatisfactory due to the lack of gait information under the real scenariowhere only one or two images may be used for recognition. In this paper, a new gait recognition framework is brought about which can combine the long-short-term attention modules on silhouette images over the whole sequence and the real human physiological information calculated by a monocular image. The contributions of this work include the following: (1) Fusing the global long-term attention (GLTA) and local short-term attention (LSTA) over the whole query sequence to improve the gait recognition accuracy, where both the short-term gait feature (from two or three frames) and long-term feature (from the whole sequence) are extracted; (2) presenting a method to calculate the real personal static and dynamic physiological features through a single monocular image; (3) by efficiently applying the human physiological information, a new physiological feature extraction (PFE) network is proposed to concatenate the physiological information with silhouette for gait recognition. Through the experiments between the CASIA-B and Multi-state Gait datasets, the effectiveness and efficiency of the proposed method are proven. Under three different walking conditions of the CASIA-B dataset, the mean accuracy of rank-1 in our method is up to 89.6%, and in the Multi-state Gait dataset, wearing different clothes, the mean accuracy of rank-1 in our method is 2.4% higher than the other works. Full article
(This article belongs to the Special Issue Sensor Technologies for Gait Analysis)
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Article
Evaluation of Arm Swing Features and Asymmetry during Gait in Parkinson’s Disease Using the Azure Kinect Sensor
Sensors 2022, 22(16), 6282; https://doi.org/10.3390/s22166282 - 21 Aug 2022
Cited by 4 | Viewed by 969
Abstract
Arm swinging is a typical feature of human walking: Continuous and rhythmic movement of the upper limbs is important to ensure postural stability and walking efficiency. However, several factors can interfere with arm swings, making walking more risky and unstable: These include aging, [...] Read more.
Arm swinging is a typical feature of human walking: Continuous and rhythmic movement of the upper limbs is important to ensure postural stability and walking efficiency. However, several factors can interfere with arm swings, making walking more risky and unstable: These include aging, neurological diseases, hemiplegia, and other comorbidities that affect motor control and coordination. Objective assessment of arm swings during walking could play a role in preventing adverse consequences, allowing appropriate treatments and rehabilitation protocols to be activated for recovery and improvement. This paper presents a system for gait analysis based on Microsoft Azure Kinect DK sensor and its body-tracking algorithm: It allows noninvasive full-body tracking, thus enabling simultaneous analysis of different aspects of walking, including arm swing characteristics. Sixteen subjects with Parkinson’s disease and 13 healthy controls were recruited with the aim of evaluating differences in arm swing features and correlating them with traditional gait parameters. Preliminary results show significant differences between the two groups and a strong correlation between the parameters. The study thus highlights the ability of the proposed system to quantify arm swing features, thus offering a simple tool to provide a more comprehensive gait assessment. Full article
(This article belongs to the Special Issue Sensor Technologies for Gait Analysis)
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Article
Criterion Validity of Linear Accelerations Measured with Low-Sampling-Frequency Accelerometers during Overground Walking in Elderly Patients with Knee Osteoarthritis
Sensors 2022, 22(14), 5289; https://doi.org/10.3390/s22145289 - 15 Jul 2022
Viewed by 1101
Abstract
Sensors with a higher sampling rate produce higher-quality data. However, for more extended periods of data acquisition, as in the continuous monitoring of patients, the handling of the generated big data becomes increasingly complicated. This study aimed to determine the validity and reliability [...] Read more.
Sensors with a higher sampling rate produce higher-quality data. However, for more extended periods of data acquisition, as in the continuous monitoring of patients, the handling of the generated big data becomes increasingly complicated. This study aimed to determine the validity and reliability of low-sampling-frequency accelerometer (SENS) measurements in patients with knee osteoarthritis. Data were collected simultaneously using SENS and a previously validated sensor (Xsens) during two repetitions of overground walking. The processed acceleration signals were compared with respect to different coordinate axes to determine the test–retest reliability and the agreement between the two systems in the time and frequency domains. In total, 44 participants were included. With respect to different axes, the interclass correlation coefficient for the repeatability of SENS measurements was [0.93–0.96]. The concordance correlation coefficients for the two systems’ agreement were [0.81–0.91] in the time domain and [0.43–0.99] in the frequency domain. The absolute biases estimated by the Bland–Altman method were [0.0005–0.008] in the time domain and [0–0.008] in the frequency domain. Low-sampling-frequency accelerometers can provide relatively valid data for measuring the gait accelerations in patients with knee osteoarthritis and can be used in the future for remote patient monitoring. Full article
(This article belongs to the Special Issue Sensor Technologies for Gait Analysis)
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Article
Flexible Machine Learning Algorithms for Clinical Gait Assessment Tools
Sensors 2022, 22(13), 4957; https://doi.org/10.3390/s22134957 - 30 Jun 2022
Viewed by 1013
Abstract
The current gold standard of gait diagnostics is dependent on large, expensive motion-capture laboratories and highly trained clinical and technical staff. Wearable sensor systems combined with machine learning may help to improve the accessibility of objective gait assessments in a broad clinical context. [...] Read more.
The current gold standard of gait diagnostics is dependent on large, expensive motion-capture laboratories and highly trained clinical and technical staff. Wearable sensor systems combined with machine learning may help to improve the accessibility of objective gait assessments in a broad clinical context. However, current algorithms lack flexibility and require large training datasets with tedious manual labelling of data. The current study tests the validity of a novel machine learning algorithm for automated gait partitioning of laboratory-based and sensor-based gait data. The developed artificial intelligence tool was used in patients with a central neurological lesion and severe gait impairments. To build the novel algorithm, 2% and 3% of the entire dataset (567 and 368 steps in total, respectively) were required for assessments with laboratory equipment and inertial measurement units. The mean errors of machine learning-based gait partitions were 0.021 s for the laboratory-based datasets and 0.034 s for the sensor-based datasets. Combining reinforcement learning with a deep neural network allows significant reduction in the size of the training datasets to <5%. The low number of required training data provides end-users with a high degree of flexibility. Non-experts can easily adjust the developed algorithm and modify the training library depending on the measurement system and clinical population. Full article
(This article belongs to the Special Issue Sensor Technologies for Gait Analysis)
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Article
Pathological-Gait Recognition Using Spatiotemporal Graph Convolutional Networks and Attention Model
Sensors 2022, 22(13), 4863; https://doi.org/10.3390/s22134863 - 27 Jun 2022
Viewed by 821
Abstract
Walking is an exercise that uses muscles and joints of the human body and is essential for understanding body condition. Analyzing body movements through gait has been studied and applied in human identification, sports science, and medicine. This study investigated a spatiotemporal graph [...] Read more.
Walking is an exercise that uses muscles and joints of the human body and is essential for understanding body condition. Analyzing body movements through gait has been studied and applied in human identification, sports science, and medicine. This study investigated a spatiotemporal graph convolutional network model (ST-GCN), using attention techniques applied to pathological-gait classification from the collected skeletal information. The focus of this study was twofold. The first objective was extracting spatiotemporal features from skeletal information presented by joint connections and applying these features to graph convolutional neural networks. The second objective was developing an attention mechanism for spatiotemporal graph convolutional neural networks, to focus on important joints in the current gait. This model establishes a pathological-gait-classification system for diagnosing sarcopenia. Experiments on three datasets, namely NTU RGB+D, pathological gait of GIST, and multimodal-gait symmetry (MMGS), validate that the proposed model outperforms existing models in gait classification. Full article
(This article belongs to the Special Issue Sensor Technologies for Gait Analysis)
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Article
Detection of Human Gait Phases Using Textile Pressure Sensors: A Low Cost and Pervasive Approach
Sensors 2022, 22(8), 2825; https://doi.org/10.3390/s22082825 - 07 Apr 2022
Viewed by 1181
Abstract
Human gait analysis is a standard method used for detecting and diagnosing diseases associated with gait disorders. Wearable technologies, due to their low costs and high portability, are increasingly being used in gait and other medical analyses. This paper evaluates the use of [...] Read more.
Human gait analysis is a standard method used for detecting and diagnosing diseases associated with gait disorders. Wearable technologies, due to their low costs and high portability, are increasingly being used in gait and other medical analyses. This paper evaluates the use of low-cost homemade textile pressure sensors to recognize gait phases. Ten sensors were integrated into stretch pants, achieving an inexpensive and pervasive solution. Nevertheless, such a simple fabrication process leads to significant sensitivity variability among sensors, hindering their adoption in precision-demanding medical applications. To tackle this issue, we evaluated the textile sensors for the classification of gait phases over three machine learning algorithms for time-series signals, namely, random forest (RF), time series forest (TSF), and multi-representation sequence learner (Mr-SEQL). Training and testing signals were generated from participants wearing the sensing pants in a test run under laboratory conditions and from an inertial sensor attached to the same pants for comparison purposes. Moreover, a new annotation method to facilitate the creation of such datasets using an ordinary webcam and a pose detection model is presented, which uses predefined rules for label generation. The results show that textile sensors successfully detect the gait phases with an average precision of 91.2% and 90.5% for RF and TSF, respectively, only 0.8% and 2.3% lower than the same values obtained from the IMU. This situation changes for Mr-SEQL, which achieved a precision of 79% for the textile sensors and 36.8% for the IMU. The overall results show the feasibility of using textile pressure sensors for human gait recognition. Full article
(This article belongs to the Special Issue Sensor Technologies for Gait Analysis)
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Article
Acoustic Environmental Conditions (Do Not?) Affect the Static Posturography Diagnostic Accuracy: A Test–Retest Reliability Study
Sensors 2022, 22(6), 2365; https://doi.org/10.3390/s22062365 - 18 Mar 2022
Viewed by 709
Abstract
Static posturography assessed with force platforms is a procedure used to obtain objective estimates related to postural adjustments. However, controlling multiple intrinsic and extrinsic factors influencing the diagnostic accuracy is essential to obtain reliable measurements and recommend its use with clinical or research [...] Read more.
Static posturography assessed with force platforms is a procedure used to obtain objective estimates related to postural adjustments. However, controlling multiple intrinsic and extrinsic factors influencing the diagnostic accuracy is essential to obtain reliable measurements and recommend its use with clinical or research purposes. We aimed to analyze how different environmental acoustic conditions affect the test–retest reliability and to analyze the most appropriate number of trials to calculate a valid mean average score. A diagnostic accuracy study was conducted enrolling 27 healthy volunteers. All procedures were taken considering consistent device settings, posture, feet position, recording time, and illumination of the room. Three trials were recorded in a silent environment (35–40 dB) and three trials were recorded in a noisy environment (85–90 dB). Results showed comparable reliability estimates for both acoustic conditions (ICC = 0.453–0.962 and 0.621–0.952), but silent conditions demonstrated better sensitivity to changes (MDC = 13.6–76%). Mean average calculations from 2 and 3 trials showed no statistically significant differences (p > 0.05). Cross-sectional studies can be conducted under noisy or silent conditions as no significantly different scores were obtained (p > 0.05) and ICC were comparable (except oscillation area). However, longitudinal studies should consider silent conditions as they demonstrated better sensitivity to real changes not derived from measurement errors. Full article
(This article belongs to the Special Issue Sensor Technologies for Gait Analysis)
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Other

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Case Report
Enhancing Free-Living Fall Risk Assessment: Contextualizing Mobility Based IMU Data
Sensors 2023, 23(2), 891; https://doi.org/10.3390/s23020891 - 12 Jan 2023
Viewed by 740
Abstract
Fall risk assessment needs contemporary approaches based on habitual data. Currently, inertial measurement unit (IMU)-based wearables are used to inform free-living spatio-temporal gait characteristics to inform mobility assessment. Typically, a fluctuation of those characteristics will infer an increased fall risk. However, current approaches [...] Read more.
Fall risk assessment needs contemporary approaches based on habitual data. Currently, inertial measurement unit (IMU)-based wearables are used to inform free-living spatio-temporal gait characteristics to inform mobility assessment. Typically, a fluctuation of those characteristics will infer an increased fall risk. However, current approaches with IMUs alone remain limited, as there are no contextual data to comprehensively determine if underlying mechanistic (intrinsic) or environmental (extrinsic) factors impact mobility and, therefore, fall risk. Here, a case study is used to explore and discuss how contemporary video-based wearables could be used to supplement arising mobility-based IMU gait data to better inform habitual fall risk assessment. A single stroke survivor was recruited, and he conducted a series of mobility tasks in a lab and beyond while wearing video-based glasses and a single IMU. The latter generated topical gait characteristics that were discussed according to current research practices. Although current IMU-based approaches are beginning to provide habitual data, they remain limited. Given the plethora of extrinsic factors that may influence mobility-based gait, there is a need to corroborate IMUs with video data to comprehensively inform fall risk assessment. Use of artificial intelligence (AI)-based computer vision approaches could drastically aid the processing of video data in a timely and ethical manner. Many off-the-shelf AI tools exist to aid this current need and provide a means to automate contextual analysis to better inform mobility from IMU gait data for an individualized and contemporary approach to habitual fall risk assessment. Full article
(This article belongs to the Special Issue Sensor Technologies for Gait Analysis)
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Systematic Review
Nonlinear Dynamic Measures of Walking in Healthy Older Adults: A Systematic Scoping Review
Sensors 2022, 22(12), 4408; https://doi.org/10.3390/s22124408 - 10 Jun 2022
Viewed by 849
Abstract
Background: Maintaining a healthy gait into old age is key to preserving the quality of life and reducing the risk of falling. Nonlinear dynamic analyses (NDAs) are a promising method of identifying characteristics of people who are at risk of falling based on [...] Read more.
Background: Maintaining a healthy gait into old age is key to preserving the quality of life and reducing the risk of falling. Nonlinear dynamic analyses (NDAs) are a promising method of identifying characteristics of people who are at risk of falling based on their movement patterns. However, there is a range of NDA measures reported in the literature. The aim of this review was to summarise the variety, characteristics and range of the nonlinear dynamic measurements used to distinguish the gait kinematics of healthy older adults and older adults at risk of falling. Methods: Medline Ovid and Web of Science databases were searched. Forty-six papers were included for full-text review. Data extracted included participant and study design characteristics, fall risk assessment tools, analytical protocols and key results. Results: Among all nonlinear dynamic measures, Lyapunov Exponent (LyE) was most common, followed by entropy and then Fouquet Multipliers (FMs) measures. LyE and Multiscale Entropy (MSE) measures distinguished between older and younger adults and fall-prone versus non-fall-prone older adults. FMs were a less sensitive measure for studying changes in older adults’ gait. Methodology and data analysis procedures for estimating nonlinear dynamic measures differed greatly between studies and are a potential source of variability in cross-study comparisons and in generating reference values. Conclusion: Future studies should develop a standard procedure to apply and estimate LyE and entropy to quantify gait characteristics. This will enable the development of reference values in estimating the risk of falling. Full article
(This article belongs to the Special Issue Sensor Technologies for Gait Analysis)
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