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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 5173

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 (4 papers)

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Research

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 616
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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Figure 1

Figure 1
<p>Example time series of thigh-angle data collected from one participant with the AccWalker app.</p> Full article ">Figure 2
<p>Histogram of the number of participants who completed assessments at 2, 3, 4, or 5 time points.</p> Full article ">Figure 3
<p>Estimated marginal mean and standard error (SE) bars of CV (%) of peak thigh flexion across time for the exposed and control groups. Asterisks indicate a value different from baseline (base).</p> Full article ">
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 1014
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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Figure 1

Figure 1
<p>Participant performing the FMA-UE assessment.</p> Full article ">Figure 2
<p>mCTSIB test instructions.</p> Full article ">Figure 3
<p>Participant performing mCTSIB test.</p> Full article ">
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 1321
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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Figure 1

Figure 1
<p>Illustration depicting the common clinical tests including (<b>top</b>-<b>left</b>) the Timed Up and Go (TUG) test, (<b>bottom</b>-<b>left</b>), the sit-to-stand (STS) test, (<b>bottom</b>-<b>middle</b>) the 10 m Walk Test (10MWT), and (<b>bottom</b>-<b>right</b>) the balance test; (<b>top</b>-<b>right</b>) placement locations of inertial sensors on the body.</p> Full article ">Figure 2
<p>Illustration of the final plot of the resultant acceleration for the three IMU sensors after identifying the start and end of the 10MWT for a sample participant.</p> Full article ">Figure 3
<p>Comparison of key outcome measures between Stroke Survivors and healthy controls in single- and dual-task conditions. (<b>Top</b>-<b>left</b>) Total TUG time (s), (<b>top</b>-<b>right</b>) right thigh RMS acceleration (m/s<sup>2</sup>) during balance testing, (<b>bottom</b>-<b>left</b>) 10MWT time (s), (<b>bottom</b>-<b>right</b>) and number of steps during the 10MWT. Error bars indicate the standard deviation. (note: The points with no common lowercase letter labels were significantly different (<span class="html-italic">p</span>-value &lt; 0.05) in the post-hoc analysis.)</p> Full article ">
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 1294
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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Figure 1

Figure 1
<p>FirstVR, a muscle deformation sensor with 14-channels based on near-infrared OMG.</p> Full article ">Figure 2
<p>Data processing flow.The recording data were normalized by subtracting the baseline. The 14-channel signals were converted to the feature values representing the skeletal muscle deformation of the flexor and extensor muscles. In total, 80–140 frames were extracted to analyze the data.</p> Full article ">Figure 3
<p>Sensed muscles by OMG. The observed flexor muscles are flexor carpi ulnaris muscle and flexor carpi radialis muscle. The observed extensor muscles are extensor carpi ulnaris muscle and extensor carpi radialis muscle.</p> Full article ">Figure 4
<p>Training types in the experiment. Gripper: grip and release 25 kg hand grippers; ball: keep gripping a ball; palm clenching: keep clenching the hand; balloon: keep gripping a balloon; and Paper: crumple a piece of newspaper.</p> Full article ">Figure 5
<p>Standard deviations of muscle deformation data for flexor muscles. Box plots of the standard deviations (SDs) of muscle deformation data for all participants’ flexor muscles. The training types were gripping–releasing of a gripper (gripper), holding of a ball (ball), palm clenching (hand), holding of a balloon (balloon), and crumpling of newspaper (paper). the symbol * means the significant difference.</p> Full article ">Figure 6
<p>Standard deviations of muscle deformation data for extensor muscles. Box plots of the standard deviations (SDs) of muscle deformation data for all participants’ extensor muscles. The training types were gripping-releasing of a gripper (gripper), holding of a ball (ball), palm clenching (hand), holding of a balloon (balloon), and crumpling of newspaper (paper). The symbol * means the significant difference.</p> Full article ">Figure 7
<p>Mean values of muscle deformation flexor muscles. Box plots of mean values of muscle deformation data of all participants’ flexor muscles. Box plots of the maximum value of the local maxima in the absolute value of the autocorrelation coefficient for flexor and extensor muscles. The training types were gripping-releasing of a gripper (gripper), holding of a ball (ball), palm clenching (hand), holding of a balloon (balloon), and crumpling of newspaper (paper).</p> Full article ">Figure 8
<p>Mean values of muscle deformation data for extensor muscles. Box plots of mean values of muscle deformation for all participants’ extensor muscles. The training types were gripping-releasing of a gripper (gripper), holding of a ball (ball), palm clenching (hand), holding of a balloon (balloon), and crumpling of newspaper (paper).</p> Full article ">Figure 9
<p>Autocorrelation coefficient in all the tasks for all participants. The graph is the correlogram, that is, the relationship between the autocorrelation coefficient value (vertical axis) and the shifted lag of the data (horizontal axis). The orange line indicates flexor muscles and the blue line indicates extensor muscles.</p> Full article ">Figure 10
<p>Local maxima in the absolute value of the autocorrelation coefficient of muscle deformation data for flexor muscles. Box plots of the maximum value of the local maxima in the absolute value of the autocorrelation coefficient for all participants’ flexor muscles. The training types were gripping-releasing of a gripper (gripper), holding of a ball (ball), palm clenching (hand), holding of a balloon (balloon), and crumpling of newspaper (paper). The symbol * means the significant difference.</p> Full article ">Figure 11
<p>Local maxima in the absolute value of the autocorrelation coefficient of muscle deformation data for extensor muscles. Box plots of the maximum value of the local maxima in the absolute value of the autocorrelation coefficient for all participants’ extensor muscles. The training types were gripping-releasing of a gripper (gripper), holding of a ball (ball), palm clenching (hand), holding of a balloon (balloon), and crumpling of newspaper (paper). The symbol * means the significant difference.</p> Full article ">
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