Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

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

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Related Special Issue
Published Papers

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

Deadline for manuscript submissions: closed (15 December 2025) | Viewed by 11839

Share This Special Issue

Special Issue Editors

Dr. Winson Lee

E-Mail Website
Guest Editor

School of Engineering, University of Wollongong, Wollongong, NSW 2522, Australia
Interests: human movement; postural control; motor-cognition relationship; rehabilitation engineering; technology for elderly people; 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 250 words) can be sent to the Editorial Office for assessment.

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

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Sensor Technology for Improving Human Movements and Postures: Part II in Sensors (9 articles)

Published Papers (6 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

27 pages, 5316 KB

Open AccessArticle

Webcam-Based Exergame for Motor Recovery with Physical Assessment via DTW

by Norapat Labchurat, Kingkarn Sookhanaphibarn, Worawat Choensawat and Pujana Paliyawan

Sensors 2026, 26(4), 1219; https://doi.org/10.3390/s26041219 - 13 Feb 2026

Cited by 1 | Viewed by 610

Abstract

This paper presents RehabHub, a home-based exergaming system that integrates standardized physical assessment directly into gameplay by using a common webcam and MediaPipe for real-time pose estimation. The system quantifies upper-limb movement quality, specifically abduction, shoulder flexion, and elbow flexion based on FMA-UE guidelines, by applying Dynamic Time Warping (DTW) together with a Z-score-based scoring model that relies on data from non-clinical adult participants. A pilot study, which included movements simulated with a 5-kg resistance band, evaluated three feature-extraction methods. The findings indicate that the single-angle method provides the clearest distinction between normal and abnormal movements, particularly for abduction and elbow flexion. In the case of shoulder flexion, the score separation was less distinct because of movement variability and posture-related angle fluctuations, which suggests that further refinement of feature design is needed. The cloud-based platform supports remote monitoring and gives caregivers access to both performance scores and recorded exercise videos. Overall, the results demonstrate the feasibility of a low-cost webcam-based assessment integrated into exergaming, and they highlight important trends for improving abnormal-movement detection in home rehabilitation systems. Full article

(This article belongs to the Special Issue Sensor Technology for Improving Human Movements and Postures: 3rd Edition)

► Show Figures

Graphical abstract

17 pages, 1161 KB

Open AccessArticle

Dual-Stream STGCN with Motion-Aware Grouping for Rehabilitation Action Quality Assessment

by Zhejun Kuang, Zhaotin Yin, Yuheng Yang, Jian Zhao and Lei Sun

Sensors 2026, 26(1), 287; https://doi.org/10.3390/s26010287 - 2 Jan 2026

Viewed by 821

Abstract

Action quality assessment automates the evaluation of human movement proficiency, which is vital for applications like sports training and rehabilitation, where objective feedback enhances patient outcomes. Action quality assessment processes motion capture data to generate quality scores for action execution. In rehabilitation exercises, joints typically work synergistically in functional groups. However, existing methods struggle to accurately model the collaborative relationships between joints. Fixed joint grouping is not flexible enough, while fully adaptive grouping lacks the guidance of prior knowledge. In this paper, based on rehabilitation theory in clinical medicine, we propose a dynamic, motion-aware grouping strategy. A two-stream architecture independently processes joint position and orientation information. Fused features are adaptively clustered into 6 functional groups by a joint motion energy-driven learnable mask generator, and intra-group temporal modeling and inter-group spatial projection are achieved through two-stage attention interaction. Our method achieves competitive results and obtains the best scores on most exercises of KIMORE, while remaining comparable on UI-PRMD. Experimental results using the KIMORE dataset show that the model outperforms current methods by reducing the mean absolute deviation by 26.5%. Ablation studies validate the necessity of dynamic grouping and the two-stream design. The core design principles of this study can be extended to fine-grained action-understanding tasks such as surgical operation assessment and motor skill quantification. Full article

(This article belongs to the Special Issue Sensor Technology for Improving Human Movements and Postures: 3rd Edition)

► Show Figures

Figure 1

24 pages, 10907 KB

Open AccessArticle

Time-Frequency Analysis of Motor Imagery During Plantar and Dorsal Flexion Movements Using a Low-Cost Ankle Exoskeleton

by Cristina Polo-Hortigüela, Mario Ortiz, Paula Soriano-Segura, Eduardo Iáñez and José M. Azorín

Sensors 2025, 25(10), 2987; https://doi.org/10.3390/s25102987 - 9 May 2025

Cited by 1 | Viewed by 2134

Abstract

Sensor technology plays a fundamental role in neuro-motor rehabilitation by enabling precise movement analysis and control. This study explores the integration of brain–machine interfaces (BMIs) and wearable sensors to enhance motor recovery in individuals with neuro-motor impairments. Specifically, different time-frequency transforms are evaluated to analyze the correlation between electroencephalographic (EEG) activity and ankle position, measured by using inertial measurement units (IMUs). A low-cost ankle exoskeleton was designed to conduct the experimental trials. Six subjects performed plantar and dorsal flexion movements while the EEG and IMU signals were recorded. The correlation between brain activity and foot kinematics was analyzed using the Short-Time Fourier Transform (STFT), Stockwell (ST), Hilbert–Huang (HHT), and Chirplet (CT) methods. The 8–20 Hz frequency band exhibited the highest correlation values. For motor imagery classification, the STFT achieved the highest accuracy (92.9%) using an EEGNet-based classifier and a state-machine approach. This study presents a dual approach: the analysis of EEG-movement correlation in different cognitive states, and the systematic comparison of four time-frequency transforms for both correlation and classification performance. The results support the potential of combining EEG and IMU data for BMI applications and highlight the importance of cognitive state in motion analysis for accessible neurorehabilitation technologies. Full article

(This article belongs to the Special Issue Sensor Technology for Improving Human Movements and Postures: 3rd Edition)

► Show Figures

Figure 1

18 pages, 5170 KB

Open AccessArticle

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

Cited by 8 | Viewed by 2201

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 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)

► Show Figures

Figure 1

11 pages, 1100 KB

Open AccessArticle

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

Cited by 1 | Viewed by 2491

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 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)

► Show Figures

Figure 1

17 pages, 5560 KB

Open AccessCommunication

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

Cited by 2 | Viewed by 2484

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 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)

► Show Figures

Journal Menu

Journal Browser

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

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Related Special Issue

Published Papers (6 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI