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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: 30 January 2025 | Viewed by 3655

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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 (3 papers)

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Research

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
Viewed by 757
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
Viewed by 994
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 1 | Viewed by 1082
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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