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Wearable Sensors for Movement, Postural Control and Locomotion Analysis
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Wearable Sensors for Movement, Postural Control and Locomotion Analysis

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

Deadline for manuscript submissions: 25 February 2026 | Viewed by 14298

Special Issue Editor

Dr. Harish Chander

E-Mail Website
Guest Editor
Neuromechanics Laboratory, Department of Kinesiology, Mississippi State University, Starkville, MS 39762, USA
Interests: human factors; ergonomics; biomechanics; motor control; fall prevention; slip, trips, and falls; postural control; balance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wearable technology has been growing at a remarkable rate in the recent years, especially for human performance assessment among sporting athletic population, clinical patient population, tactical military population, as well as occupational population. Several different wearable devices such as inertial measurement units (IMUs), accelerometers, gyroscopes, magnetometers, pedometers, electric goniometers, heart rate monitors, sleep monitoring sensors, physical activity sensors, and virtual, augmented, and extended reality wearables, are used for assessment of various biomechanical, physiological, and cognitive performance. In addition to these wearable devices, sensors such as foot pressure sensors, smart socks, smart insoles, as well as smart phone application using wearable sensor technologies have been used to assess an individual’s postural control/stability and locomotion/gait in various settings. The use of wearable sensors to assess and analyze balance and gait among athletic, clinical, tactical, and occupational populations, aids in better understanding of the functional status of the postural control and locomotor system, and thereby plan and provide appropriate care and rehabilitation.

With research in wearable sensors constantly evolving, this Special Issue “Wearable Sensors for Movement, Postural Control and Locomotion Analysis” will focus on the application of principles of neuroscience, biomechanics, motor control, biomedical engineering, human factors, ergonomics, public health, and epidemiology for analyses of postural control and locomotion using wearable sensors in various populations. A wide range of topics addressing methods for preventive monitoring, assessment, detection, intervention, and rehabilitation for postural control and locomotion among any populations will be covered. Contributions including empirical research, review articles, case reports, etc. on advances in fall prevention are encouraged.

Dr. Harish Chander
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 2600 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.

Keywords

  • posture
  • balance
  • gait
  • wearables
  • technology

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Published Papers (9 papers)

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Research

Jump to: Review

12 pages, 2771 KiB  
Article
Multiple Sclerosis Classification Using the Local Divergence Exponent: Parameters Selection for State-Space Reconstruction
by L. Eduardo Cofré Lizama, Liuhua Peng, Tomas Kalincik, Mary P. Galea and Maya G. Panisset
Sensors 2025, 25(9), 2819; https://doi.org/10.3390/s25092819 - 30 Apr 2025
Viewed by 140
Abstract
Background: Using the local divergence exponent (LDE), it has been concluded that walking stability is impaired in people with multiple sclerosis (pwMS). However, the use of several calculation approaches hinders comparisons across studies. We aimed to determine whether using different parameters for state [...] Read more.
Background: Using the local divergence exponent (LDE), it has been concluded that walking stability is impaired in people with multiple sclerosis (pwMS). However, the use of several calculation approaches hinders comparisons across studies. We aimed to determine whether using different parameters for state space reconstruction to calculate LDE affects the classification of pwMS. Methods: A total of 55 pwMS and 23 controls walked up and down a 20 m corridor for 5 min. The LDE was calculated using three different combinations of n-dimensions (dE) and time delays (τ): (a) trial-specific, (b) median across subjects, and (c) fixed dE = 5 and τ = 10. The LDE was calculated using vertical (VT), mediolateral (ML), and anteroposterior (AP) accelerations, the norm (N), and 3D data from sensors placed on the sternum and lumbar. Classification accuracy across results obtained with different parameter combinations was compared using a Quadratic Discriminant Analysis (QDA). Results: The best classification accuracy, 84%, was achieved when using the LDE obtained with norm acceleration data from the sternum sensor with a fixed dE = 5 and τ = 10 and considering speed as a covariate. Lumbar LDEs were less accurate than sternum LDEs. Conclusions: LDEs calculated with a fixed dE = 5 and τ = 10 for the norm acceleration from a sternum-placed sensor can best classify pwMS. Using fixed parameters for the state space reconstruction, and consequently LDE calculation, can simplify the implementation of the LDE as a mobility biomarker in MS and provides evidence for future consensus for its calculation. Full article
(This article belongs to the Special Issue Wearable Sensors for Movement, Postural Control and Locomotion Analysis)
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14 pages, 218 KiB  
Article
The Effects of Palmar Cooling on Repeated Sprinting Ability: A Randomized Controlled Clinical Trial
by Michael Brown, Jacob Daniels, Marli Crabtree, Kenneth Thompson, Joshua Murphy, William Pannell and Ryan McGlawn
Sensors 2025, 25(6), 1830; https://doi.org/10.3390/s25061830 - 15 Mar 2025
Viewed by 1651
Abstract
Evidence supports the role of palmar cooling to improve exercise performance, especially with endurance and resistance activities. The aim of this randomized placebo-controlled trial was to explore the effects of palmar cooling on repeated sprinting performance and recovery. Fifteen graduate students were randomly [...] Read more.
Evidence supports the role of palmar cooling to improve exercise performance, especially with endurance and resistance activities. The aim of this randomized placebo-controlled trial was to explore the effects of palmar cooling on repeated sprinting performance and recovery. Fifteen graduate students were randomly assigned to either a palmar cooling intervention or placebo group (males: n = 8, females: n = 7; Avg. age: 24.06 yrs.) After a ten-minute warm-up, participants completed ten sixty-meter sprints that included two 180-degree changes of direction. Three bouts of two-minute intervention or placebo occurred during the study. Data for sprint times, heart rate, and RPE were collected throughout testing. A muscle soreness rating was collected via survey 48 h post intervention. Statistically and practically significant differences were found between groups for average sprint times, heart rate, and delayed onset muscle soreness. The intervention group utilizing palmar cooling demonstrated less degradation in sprint times, lower heart rate upon completion, and a lower soreness rate 48 h after testing. More research is needed with a larger sample size to determine if practical and statistically significant differences will be maintained and would allow for a more robust multivariant analysis, resulting in the findings being more generalizable to a larger population. Full article
(This article belongs to the Special Issue Wearable Sensors for Movement, Postural Control and Locomotion Analysis)
19 pages, 1971 KiB  
Article
A Hierarchical-Based Learning Approach for Multi-Action Intent Recognition
by David Hollinger, Ryan S. Pollard, Mark C. Schall, Jr., Howard Chen and Michael Zabala
Sensors 2024, 24(23), 7857; https://doi.org/10.3390/s24237857 - 9 Dec 2024
Viewed by 981
Abstract
Recent applications of wearable inertial measurement units (IMUs) for predicting human movement have often entailed estimating action-level (e.g., walking, running, jumping) and joint-level (e.g., ankle plantarflexion angle) motion. Although action-level or joint-level information is frequently the focus of movement intent prediction, contextual information [...] Read more.
Recent applications of wearable inertial measurement units (IMUs) for predicting human movement have often entailed estimating action-level (e.g., walking, running, jumping) and joint-level (e.g., ankle plantarflexion angle) motion. Although action-level or joint-level information is frequently the focus of movement intent prediction, contextual information is necessary for a more thorough approach to intent recognition. Therefore, a combination of action-level and joint-level information may offer a more comprehensive approach to predicting movement intent. In this study, we devised a novel hierarchical-based method combining action-level classification and subsequent joint-level regression to predict joint angles 100 ms into the future. K-nearest neighbors (KNN), bidirectional long short-term memory (BiLSTM), and temporal convolutional network (TCN) models were employed for action-level classification, and a random forest model trained on action-specific IMU data was used for joint-level prediction. A joint-level action-generic model trained on multiple actions (e.g., backward walking, kneeling down, kneeling up, running, and walking) was also used for predicting the joint angle. Compared with a hierarchical-based approach, the action-generic model had lower prediction error for backward walking, kneeling down, and kneeling up. Although the TCN and BiLSTM classifiers achieved classification accuracies of 89.87% and 89.30%, respectively, they did not surpass the performance of the action-generic random forest model when used in combination with an action-specific random forest model. This may have been because the action-generic approach was trained on more data from multiple actions. This study demonstrates the advantage of leveraging large, disparate data sources over a hierarchical-based approach for joint-level prediction. Moreover, it demonstrates the efficacy of an IMU-driven, task-agnostic model in predicting future joint angles across multiple actions. Full article
(This article belongs to the Special Issue Wearable Sensors for Movement, Postural Control and Locomotion Analysis)
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11 pages, 963 KiB  
Article
An Objective Assessment of Neuromotor Control Using a Smartphone App After Repeated Subconcussive Blast Exposure
by Charlend K. Howard, Masahiro Yamada, Marcia Dovel, Rie Leverett, Alexander Hill, Kenneth A. Manlapaz, David O. Keyser, Rene S. Hernandez, Sheilah S. Rowe, Walter S. Carr, Michael J. Roy and Christopher K. Rhea
Sensors 2024, 24(21), 7064; https://doi.org/10.3390/s24217064 - 2 Nov 2024
Viewed by 1326
Abstract
Subconcussive blast exposure has been shown to alter neurological functioning. However, the extent to which neurological dysfunction persists after blast exposure is unknown. This longitudinal study examined the potential short- and long-term effects of repeated subconcussive blast exposure on neuromotor performance from heavy [...] Read more.
Subconcussive blast exposure has been shown to alter neurological functioning. However, the extent to which neurological dysfunction persists after blast exposure is unknown. This longitudinal study examined the potential short- and long-term effects of repeated subconcussive blast exposure on neuromotor performance from heavy weapons training in military personnel. A total of 214 participants were assessed; 137 were exposed to repeated subconcussive blasts and 77 were not exposed to blasts (controls). Participants completed a short stepping-in-place task while an Android smartphone app placed on their thigh recorded movement kinematics. We showed acute suppression of neuromotor variability 6 h after subconcussive blast exposure, followed by a rebound to levels not different from baseline at the 72 h, 2-week, and 3-month post-tests. It is postulated that this suppression of neuromotor variability results from a reduction in the functional degrees of freedom from the subconcussive neurological insult. It is important to note that this change in behavior is short-lived, with a return to pre-blast exposure movement kinematics within 72 h. Full article
(This article belongs to the Special Issue Wearable Sensors for Movement, Postural Control and Locomotion Analysis)
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14 pages, 1852 KiB  
Article
Influence of Impaired Upper Extremity Motor Function on Static Balance in People with Chronic Stroke
by Ana Mallo-López, Alicia Cuesta-Gómez, Teresa E. Fernández-Pardo, Ángela Aguilera-Rubio and Francisco Molina-Rueda
Sensors 2024, 24(13), 4311; https://doi.org/10.3390/s24134311 - 2 Jul 2024
Cited by 1 | Viewed by 1610
Abstract
Background: Stroke is a leading cause of disability, especially due to an increased fall risk and postural instability. The objective of this study was to analyze the impact of motor impairment in the hemiparetic UE on static balance in standing, in subject with [...] Read more.
Background: Stroke is a leading cause of disability, especially due to an increased fall risk and postural instability. The objective of this study was to analyze the impact of motor impairment in the hemiparetic UE on static balance in standing, in subject with chronic stroke. Methods: Seventy adults with chronic stroke, capable of independent standing and walking, participated in this cross-sectional study. The exclusion criteria included vestibular, cerebellar, or posterior cord lesions. The participants were classified based on their UE impairment using the Fugl-Meyer Assessment of Motor Recovery after Stroke (FMA-UE). A posturographic evaluation (mCTSIB) was performed in the standing position to analyze the center of pressure (COP) displacement in the mediolateral (ML) and anteroposterior (AP) axes and its mean speed with eyes open (OE) and closed (EC) on stable and unstable surfaces. Results: A strong and significant correlation (r = −0.53; p < 0.001) was observed between the mediolateral (ML) center of pressure (COP) oscillation and the FMA-UE, which was particularly strong with eyes closed [r(EO) = 0.5; r(EC) = 0.54]. The results of the multiple linear regression analysis indicated that the ML oscillation is influenced significantly by the FMA-Motor, and specifically by the sections on UE, wrist, coordination/speed, and sensation. Conclusions: The hemiparetic UE motor capacity is strongly related to the ML COP oscillation during standing in individuals with chronic stroke, with a lower motor capacity associated with a greater instability. Understanding these relationships underpins the interventions to improve balance and reduce falls in people who have had a stroke. Full article
(This article belongs to the Special Issue Wearable Sensors for Movement, Postural Control and Locomotion Analysis)
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13 pages, 1728 KiB  
Article
Dual Tasking Affects the Outcomes of Instrumented Timed up and Go, Sit-to-Stand, Balance, and 10-Meter Walk Tests in Stroke Survivors
by Masoud Abdollahi, Pranav Madhav Kuber and Ehsan Rashedi
Sensors 2024, 24(10), 2996; https://doi.org/10.3390/s24102996 - 9 May 2024
Cited by 2 | Viewed by 2662
Abstract
Stroke can impair mobility, with deficits more pronounced while simultaneously performing multiple activities. In this study, common clinical tests were instrumented with wearable motion sensors to study motor–cognitive interference effects in stroke survivors (SS). A total of 21 SS and 20 healthy controls [...] Read more.
Stroke can impair mobility, with deficits more pronounced while simultaneously performing multiple activities. In this study, common clinical tests were instrumented with wearable motion sensors to study motor–cognitive interference effects in stroke survivors (SS). A total of 21 SS and 20 healthy controls performed the Timed Up and Go (TUG), Sit-to-Stand (STS), balance, and 10-Meter Walk (10MWT) tests under single and dual-task (counting backward) conditions. Calculated measures included total time and gait measures for TUG, STS, and 10MWT. Balance tests for both open and closed eyes conditions were assessed using sway, measured using the linear acceleration of the thorax, pelvis, and thighs. SS exhibited poorer performance with slower TUG (16.15 s vs. 13.34 s, single-task p < 0.001), greater sway in the eyes open balance test (0.1 m/s2 vs. 0.08 m/s2, p = 0.035), and slower 10MWT (12.94 s vs. 10.98 s p = 0.01) compared to the controls. Dual tasking increased the TUG time (~14%, p < 0.001), balance thorax sway (~64%, p < 0.001), and 10MWT time (~17%, p < 0.001) in the SS group. Interaction effects were minimal, suggesting similar dual-task costs. The findings demonstrate exaggerated mobility deficits in SS during dual-task clinical testing. Dual-task assessments may be more effective in revealing impairments. Integrating cognitive challenges into evaluation can optimize the identification of fall risks and personalize interventions targeting identified cognitive–motor limitations post stroke. Full article
(This article belongs to the Special Issue Wearable Sensors for Movement, Postural Control and Locomotion Analysis)
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15 pages, 2840 KiB  
Article
Optical Myography-Based Sensing Methodology of Application of Random Loads to Muscles during Hand-Gripping Training
by Tamon Miyake, Tomohito Minakuchi, Suguru Sato, Chihiro Okubo, Dai Yanagihara and Emi Tamaki
Sensors 2024, 24(4), 1108; https://doi.org/10.3390/s24041108 - 8 Feb 2024
Cited by 3 | Viewed by 1921
Abstract
Hand-gripping training is important for improving the fundamental functions of human physical activity. Bernstein’s idea of “repetition without repetition” suggests that motor control function should be trained under changing states. The randomness level of load should be visualized for self-administered screening when repeating [...] Read more.
Hand-gripping training is important for improving the fundamental functions of human physical activity. Bernstein’s idea of “repetition without repetition” suggests that motor control function should be trained under changing states. The randomness level of load should be visualized for self-administered screening when repeating various training tasks under changing states. This study aims to develop a sensing methodology of random loads applied to both the agonist and antagonist skeletal muscles when performing physical tasks. We assumed that the time-variability and periodicity of the applied load appear in the time-series feature of muscle deformation data. In the experiment, 14 participants conducted the gripping tasks with a gripper, ball, balloon, Palm clenching, and paper. Crumpling pieces of paper (paper exercise) involves randomness because the resistance force of the paper changes depending on the shape and layers of the paper. Optical myography during gripping tasks was measured, and time-series features were analyzed. As a result, our system could detect the random movement of muscles during training. Full article
(This article belongs to the Special Issue Wearable Sensors for Movement, Postural Control and Locomotion Analysis)
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Review

Jump to: Research

16 pages, 1107 KiB  
Review
Potential for Wearable Sensor-Based Field-Deployable Diagnosis and Monitoring of Mild Traumatic Brain Injury: A Scoping Review
by Hope C. Davis-Wilson, Erika Maldonado-Rosado, Meghan Hegarty-Craver and Dorota S. Temple
Sensors 2025, 25(9), 2803; https://doi.org/10.3390/s25092803 - 29 Apr 2025
Viewed by 361
Abstract
Studies have shown that wearable commercial off-the-shelf sensors, such as accelerometers, inertial measurement units, and heart monitors, can distinguish between individuals with a mild traumatic brain injury (mTBI) and uninjured controls. However, there is no consensus on which metrics derived from wearable sensors [...] Read more.
Studies have shown that wearable commercial off-the-shelf sensors, such as accelerometers, inertial measurement units, and heart monitors, can distinguish between individuals with a mild traumatic brain injury (mTBI) and uninjured controls. However, there is no consensus on which metrics derived from wearable sensors are best to use for objective identification of mTBI symptoms. The primary aim of this scoping review was to map the state of knowledge of wearable sensor-based assessments for mTBI, based on previously published research. Data sources included Web of Science and PubMed. Original peer-reviewed articles were selected if mTBI was clinically diagnosed, an uninjured control cohort was included, and data collection used at least one digital sensor worn on the body. After screening 507 articles, 21 studies were included in the analysis. Overall, the studies identified multiple wearables-derived physiological metrics that differ between individuals with mTBI and uninjured controls. Some metrics associated with static balance, walking tasks, and postural changes to initiate an autonomic nervous system response were shown to support diagnosis of mTBI in retrospective studies with acceptable to outstanding accuracy. Further studies are needed to formulate standard protocols, reproduce results in large heterogeneous cohorts in prospective studies, and develop improved models that can diagnose mTBI with sufficient sensitivity and specificity in targeted populations. Full article
(This article belongs to the Special Issue Wearable Sensors for Movement, Postural Control and Locomotion Analysis)
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35 pages, 13196 KiB  
Review
Enhancing Intelligent Shoes with Gait Analysis: A Review on the Spatiotemporal Estimation Techniques
by Anna M. Joseph, Azadeh Kian and Rezaul Begg
Sensors 2024, 24(24), 7880; https://doi.org/10.3390/s24247880 - 10 Dec 2024
Cited by 1 | Viewed by 2334
Abstract
The continuous, automated monitoring of sensor-based data for walking capacity and mobility has expanded gait analysis applications beyond controlled laboratory settings to real-world, everyday environments facilitated by the development of portable, cost-efficient wearable sensors. In particular, the integration of Inertial Measurement Units (IMUs) [...] Read more.
The continuous, automated monitoring of sensor-based data for walking capacity and mobility has expanded gait analysis applications beyond controlled laboratory settings to real-world, everyday environments facilitated by the development of portable, cost-efficient wearable sensors. In particular, the integration of Inertial Measurement Units (IMUs) into smart shoes has proven effective for capturing detailed foot movements and spatiotemporal gait characteristics. While IMUs enable accurate foot trajectory estimation through the double integration of acceleration data, challenges such as drift errors necessitate robust correction techniques to ensure reliable performance. This review analyzes current literature on shoe-based systems utilizing IMUs to estimate spatiotemporal gait parameters and foot trajectory characteristics, including foot–ground clearance. We explore the challenges and advancements in achieving accurate 3D foot trajectory estimation using IMUs in smart shoes and the application of advanced techniques like zero-velocity updates and error correction methods. These developments present significant opportunities for achieving reliable and efficient real-time gait assessment in everyday environments. Full article
(This article belongs to the Special Issue Wearable Sensors for Movement, Postural Control and Locomotion Analysis)
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