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Wearable Sensors in Biomechanics and Human Motion
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Wearable Sensors in Biomechanics and Human Motion

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

Deadline for manuscript submissions: 15 February 2026 | Viewed by 2483

Special Issue Editors

Dr. Philippe Dixon

E-Mail Website
Guest Editor
Department of Kinesiology and Physical Education, McGill University, Montreal, QC H3A 0G4, Canada
Interests: biomechanics; motion capture; gait analysis; machine learning; wearable sensors; running
Dr. Clint Hansen

E-Mail Website
Guest Editor
Department of Neurology, University Hospital Schleswig-Holstein Campus Kiel and Kiel University, 24105 Kiel, Germany
Interests: biomechanics; motor control
Special Issues, Collections and Topics in MDPI journals
Dr. Jill Emmerzaal

E-Mail Website
Guest Editor
Department of Kinesiology and Physical Education, McGill University, Montreal, QC H3A 0G4, Canada
Interests: rehabilitation; musculoskeletal biomechanics

Special Issue Information

Dear Colleagues,

This Special Issue aims to highlight recent advances in the use of wearable sensors in biomechanics and human motion research across diverse settings and populations. Wearable technologies, such as inertial measurement units (IMUs), electromyography (EMG), and pressure insoles, are transforming how we assess movement biomechanics in real-world environments. We invite contributions that explore methodological innovations, data processing algorithms, and applications of wearable systems in clinical, sports, occupational, or daily-life contexts.

Topics of interest include, but are not limited to, the following: wearable sensor-based gait and posture analysis, motion tracking, joint kinematic and joint kinetic estimation, activity recognition, rehabilitation monitoring, injury prevention, and integration with machine learning or digital health platforms. Both original research and reviews are welcome.

The goal of this issue is to provide a multidisciplinary forum for researchers, clinicians, and engineers to share cutting-edge research developments that advance the understanding and application of wearable sensor technologies in biomechanics and human movement science.

Dr. Philippe Dixon
Dr. Clint Hansen
Dr. Jill Emmerzaal
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

  • biomechanics
  • wearable sensors
  • human motion analysis
  • activity recognition
  • machine learning
  • digital health
  • sport science
  • gait and posture
  • inertial measurement units

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Published Papers (2 papers)

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16 pages, 2273 KB  
Article
Joint Function and Movement Variability During Daily Living Activities Performed Throughout the Home Setting: A Digital Twin Modeling Study
by Zhou Fang, Mohammad Yavari, Yiqun Chen, Davood Shojaei, Peter Vee Sin Lee, Abbas Rajabifard and David Ackland
Sensors 2025, 25(24), 7409; https://doi.org/10.3390/s25247409 - 5 Dec 2025
Viewed by 327
Abstract
Human mobility is commonly assessed in the laboratory environment, but accurate and robust joint motion measurement and task classification in the home setting are rarely undertaken. This study aimed to develop a digital twin model of a home to measure, visualize, and classify [...] Read more.
Human mobility is commonly assessed in the laboratory environment, but accurate and robust joint motion measurement and task classification in the home setting are rarely undertaken. This study aimed to develop a digital twin model of a home to measure, visualize, and classify joint motion during activities of daily living. A fully furnished single-bedroom apartment was digitally reconstructed using 3D photogrammetry. Ten healthy adults performed 19 activities of daily living over a 2 h period throughout the apartment. Each participant’s upper and lower limb joint motion was measured using inertial measurement units, and body spatial location was measured using an ultra-wide band sensor, registered to the digital home model. Supervised machine learning classified tasks with a mean 82.3% accuracy. Hair combing involved the highest range of shoulder elevation (124.2 ± 21.2°), while sit-to-stand exhibited both the largest hip flexion (75.7 ± 10.3°) and knee flexion (91.8 ± 8.6°). Joint motion varied from room to room, even for a given task. For example, subjects walked fastest in the living room (1.0 ± 0.2 m/s) and slowest in the bathroom (0.78 ± 0.10 m/s), while the mean maximum ankle dorsiflexion in the living room was significantly higher than that in the bathroom (mean difference: 4.9°, p = 0.002, Cohen’s d = 1.25). This study highlights the dependency of both upper and lower limb joint motion during activities of daily living on the internal home environment. The digital twin modeling framework reported may be useful in planning home-based rehabilitation, remote monitoring, and for interior design and ergonomics. Full article
(This article belongs to the Special Issue Wearable Sensors in Biomechanics and Human Motion)
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14 pages, 1964 KB  
Article
Bridging the Methodological Gap Between Inertial Sensors and Optical Motion Capture: Deep Learning as the Path to Accurate Joint Kinematic Modelling Using Inertial Sensors
by Vaibhav R. Shah and Philippe C. Dixon
Sensors 2025, 25(18), 5728; https://doi.org/10.3390/s25185728 - 14 Sep 2025
Viewed by 1075
Abstract
As advancements in inertial measurement units (IMUs) for motion analysis progress, the inability to directly apply decades of research-based optical motion capture (OMC) methodologies presents a significant challenge. This study aims to bridge this gap by proposing an innovative deep learning approach to [...] Read more.
As advancements in inertial measurement units (IMUs) for motion analysis progress, the inability to directly apply decades of research-based optical motion capture (OMC) methodologies presents a significant challenge. This study aims to bridge this gap by proposing an innovative deep learning approach to predict marker positions from IMU data, allowing traditional OMC-based calculations to estimate joint kinematics. Eighteen participants walked on a treadmill with seven IMUs and retroreflective markers. Trials were divided into normalized gait cycles (101 frames), and an autoencoder network with a custom Biomech loss function was used to predict 16 marker positions from IMU data. The model was validated using the leave-one-subject-out method and assessed using root mean squared error (RMSE). Joint angles in the sagittal plane were calculated using OMC methods, and RMSE was computed with and without alignment using dynamic time warping (DTW). The models were also tested on external datasets. Marker predictions achieved RMSE values of 2–4 cm, enabling joint angle predictions with 4–7° RMSE without alignment and 2–4° RMSE after DTW for sagittal plane joint angles (ankle, knee, hip). Validation using separate and open-source datasets confirmed the model’s generalizability, with similar RMSE values across datasets (4–7° RMSE without DTW and 2–4° with DTW). This study demonstrates the feasibility of applying conventional biomechanical models to IMUs, enabling accurate movement analysis and visualization outside controlled environments. This approach to predicting marker positions helps to bridge the gap between IMUs and OMC systems, enabling decades of research-based biomechanical methodologies to be applied to IMU data. Full article
(This article belongs to the Special Issue Wearable Sensors in Biomechanics and Human Motion)
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