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Sensor Technology for Improving Human Movements and Postures: 3rd Edition
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Sensor Technology for Improving Human Movements and Postures: 3rd Edition

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

Deadline for manuscript submissions: 15 December 2025 | Viewed by 2145

Special Issue Editors

Dr. Winson Lee

E-Mail Website
Guest Editor
School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Wollongong, NSW 2522, Australia
Interests: rehabilitation engineering; technology for elderly people; human movement; postural control; prosthetics; orthotics
Special Issues, Collections and Topics in MDPI journals
Dr. Emre Sariyildiz

E-Mail Website
Guest Editor
School of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Wollongong, NSW 2522, Australia
Interests: motion control; robotics and biomectronics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensor technology can be used to measure movements and postures. Such measurements can potentially improve musculoskeletal health, leading to better quality of life in areas of gerontology, physical rehabilitation, sports, and occupations requiring physical movements or prolonged static postures. For example, sensors can be used to

  • Assist or encourage walking and prevent falls of older adults;
  • Enable exoskeletal or robotic devices to improve mobility in people with neuro-musculoskeletal disorder;
  • Detect sport-specific movements to improve sports performance and reduce injury risk;
  • Improve occupational biomechanics and ergonomics.

Examples of sensors include accelerometers, gyroscopes, magnetometers, and force sensors. They can be wearable or laboratory-based.

This Special Issue focuses on the developments, uses, and/or outcome measurements of sensor technology, including wearable sensors with or without biofeedback, lab-based sensing systems for forces and motions, biorobotic sensors, and smart prosthetic and orthotic devices, which ultimately aim to improve human movements and/or sport performance. Original research and review papers in these areas are encouraged.

Dr. Winson Lee
Dr. Emre Sariyildiz
Guest Editors

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

  • wearable sensors
  • robotic sensors
  • motion analysis
  • rehabilitation
  • aging
  • sports and injury

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Related Special Issue

Published Papers (3 papers)

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Research

20 pages, 5170 KiB  
Article
Non-Intrusive Monitoring and Detection of Mobility Loss in Older Adults Using Binary Sensors
by Ioan Susnea, Emilia Pecheanu, Adina Cocu, Adrian Istrate, Catalin Anghel and Paul Iacobescu
Sensors 2025, 25(9), 2755; https://doi.org/10.3390/s25092755 - 26 Apr 2025
Viewed by 90
Abstract
(1) Background and objective: Mobility is crucial for healthy aging, and its loss significantly impacts the quality of life, healthcare costs, and mortality among older adults. Clinical mobility assessment methods, though precise, are resource-intensive and economically impractical, and most of the existing solutions [...] Read more.
(1) Background and objective: Mobility is crucial for healthy aging, and its loss significantly impacts the quality of life, healthcare costs, and mortality among older adults. Clinical mobility assessment methods, though precise, are resource-intensive and economically impractical, and most of the existing solutions for automatic detection of mobility anomalies are either obtrusive or improper for long time monitoring. This study explores the feasibility of using non-intrusive, low-cost binary sensors for continuous, remote detection of mobility anomalies in older adults, aiming to identify both sudden mobility events and gradual mobility loss. (2) Method: The study utilized publicly available datasets (CASAS Aruba and HH120) containing annotated activity data recorded from binary sensors installed in residential environments. After data preprocessing—including filtering irrelevant sensor events and aggregation into behaviorally meaningful places (BMPs)—a time series forecasting model (Prophet) was used to predict normal mobility patterns. A fuzzy inference module analyzed deviations between observed and predicted sensor data to determine the probability of mobility anomalies. (3) Results: The system effectively identified periods of prolonged inactivity indicative of potential falls or other mobility disruptions. Preliminary evaluation indicated a detection rate of approximately 77–81% for point mobility anomalies, with a false positive rate ranging from 12 to 16%. Additionally, the approach successfully detected simulated gradual declines in mobility (1% per day reduction), evidenced by statistically significant regression trends in activity levels over time. (4) Conclusions: The study argues that non-intrusive binary sensors, combined with lightweight forecasting models and fuzzy inference, may provide a practical and scalable solution for detecting mobility anomalies in older adults. Although performance can be further enhanced through improved data preprocessing, predictive modeling, and anomaly threshold tuning, the proposed system effectively addresses key limitations of existing mobility assessment approaches. Full article
(This article belongs to the Special Issue Sensor Technology for Improving Human Movements and Postures: 3rd Edition)
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11 pages, 1100 KiB  
Article
Clinical Whole-Body Gait Characterization Using a Single RGB-D Sensor
by Lukas Boborzi, Johannes Bertram, Roman Schniepp, Julian Decker and Max Wuehr
Sensors 2025, 25(2), 333; https://doi.org/10.3390/s25020333 - 8 Jan 2025
Viewed by 831
Abstract
Instrumented gait analysis is widely used in clinical settings for the early detection of neurological disorders, monitoring disease progression, and evaluating fall risk. However, the gold-standard marker-based 3D motion analysis is limited by high time and personnel demands. Advances in computer vision now [...] Read more.
Instrumented gait analysis is widely used in clinical settings for the early detection of neurological disorders, monitoring disease progression, and evaluating fall risk. However, the gold-standard marker-based 3D motion analysis is limited by high time and personnel demands. Advances in computer vision now enable markerless whole-body tracking with high accuracy. Here, we present vGait, a comprehensive 3D gait assessment method using a single RGB-D sensor and state-of-the-art pose-tracking algorithms. vGait was validated in healthy participants during frontal- and sagittal-perspective walking. Performance was comparable across perspectives, with vGait achieving high accuracy in detecting initial and final foot contacts (F1 scores > 95%) and reliably quantifying spatiotemporal gait parameters (e.g., stride time, stride length) and whole-body coordination metrics (e.g., arm swing and knee angle ROM) at different levels of granularity (mean, step-to-step variability, side asymmetry). The flexibility, accuracy, and minimal resource requirements of vGait make it a valuable tool for clinical and non-clinical applications, including outpatient clinics, medical practices, nursing homes, and community settings. By enabling efficient and scalable gait assessment, vGait has the potential to enhance diagnostic and therapeutic workflows and improve access to clinical mobility monitoring. Full article
(This article belongs to the Special Issue Sensor Technology for Improving Human Movements and Postures: 3rd Edition)
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17 pages, 5560 KiB  
Communication
Leveraging Sensor Technology to Characterize the Postural Control Spectrum
by Christopher Aliperti, Josiah Steckenrider, Darius Sattari, James Peterson, Caspian Bell and Rebecca Zifchock
Sensors 2024, 24(23), 7420; https://doi.org/10.3390/s24237420 - 21 Nov 2024
Viewed by 871
Abstract
The purpose of this paper is to describe ongoing research on appropriate instrumentation and analysis techniques to characterize postural stability, postural agility, and dynamic stability, which collectively comprise the postural control spectrum. This study had a specific focus on using emerging sensors to [...] Read more.
The purpose of this paper is to describe ongoing research on appropriate instrumentation and analysis techniques to characterize postural stability, postural agility, and dynamic stability, which collectively comprise the postural control spectrum. This study had a specific focus on using emerging sensors to develop protocols suitable for use outside laboratory or clinical settings. First, we examined the optimal number and placement of wearable accelerometers for assessing postural stability. Next, we proposed metrics and protocols for assessing postural agility with the use of a custom force plate-controlled video game. Finally, we proposed a method to quantify dynamic stability during walking tasks using novel frequency-domain metrics extracted from acceleration data obtained with a single body-worn IMU. In each of the three studies, a surrogate for instability was introduced, and the sensors and metrics discussed in this paper show promise for differentiating these trials from stable condition trials. Next steps for this work include expanding the tested population size and refining the methods to even more reliably and unobtrusively characterize postural control status in a variety of scenarios. Full article
(This article belongs to the Special Issue Sensor Technology for Improving Human Movements and Postures: 3rd Edition)
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