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Advanced Sensors in Biomechanics and Rehabilitation—2nd Edition
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Advanced Sensors in Biomechanics and Rehabilitation—2nd Edition

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

Deadline for manuscript submissions: closed (20 May 2026) | Viewed by 1876

Special Issue Editor

Prof. Dr. Thurmon Lockhart

E-Mail Website
Guest Editor
School of Biological and Health Systems Engineering, Ira A Fulton Schools of Engineering, Arizona State University, Tempe, AZ, USA
Interests: gait and posture; activity monitoring; fall risk assessment; nonlinear dynamics; biodynamics; wireless inertial sensors; wearables; musculoskeletal and neuro-rehabilitation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to unraveling the transformative role of sensor technologies in biomechanics and rehabilitation. In an era where precision and personalized care are paramount, these sophisticated tools significantly enhance our ability to understand human motion mechanics and develop effective therapeutic interventions.

The adoption of sensors in biomechanical studies allows for detailed analyses of body posture, gait, muscle activation, and joint kinematics, leading to more accurate diagnoses and tailored treatments. Moreover, in rehabilitation, sensors facilitate the real-time tracking of patients' functional abilities, thus informing clinicians on the efficacy of therapeutic approaches and guiding necessary adjustments. Wearable sensors, for instance, can provide valuable insights into patients' daily activities, helping determine the effectiveness of prescribed exercises and further enhancing rehabilitation outcomes.

This Special Issue aims to shed light on current research exploring the application of sensing technology in biomechanics and rehabilitation. We invite you to submit original research papers to the issue.

Prof. Dr. Thurmon Lockhart
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 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
  • biomechanics
  • rehabilitation
  • gait analysis
  • posture

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Related Special Issue

Published Papers (2 papers)

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Research

14 pages, 18688 KB  
Article
Outdoor Motion Capture at Scale
by Michael Zwölfer, Martin Mössner, Helge Rhodin and Werner Nachbauer
Sensors 2026, 26(6), 1951; https://doi.org/10.3390/s26061951 - 20 Mar 2026
Viewed by 494
Abstract
Capturing kinematic data in outdoor sports is challenging, as motions span large capture volumes and occur under difficult environmental conditions. Video-based approaches, particularly with pan–tilt–zoom cameras, offer a practical solution, but the extensive manual post-processing required limits their use to short sequences and [...] Read more.
Capturing kinematic data in outdoor sports is challenging, as motions span large capture volumes and occur under difficult environmental conditions. Video-based approaches, particularly with pan–tilt–zoom cameras, offer a practical solution, but the extensive manual post-processing required limits their use to short sequences and few athletes. This study presents a motion capture pipeline that automates the detection of both reference points and sport-specific keypoints to overcome this limitation. The field test employed eight cameras covering a 250×80×30 m capture volume with nearly 300 reference points. Ten state-certified ski instructors performed eight standardized maneuvers. Reference points were localized through a hybrid approach combining YOLO object detection and ArUco marker identification. AlphaPose was fine-tuned on a new manually annotated dataset to detect skier-specific keypoints (e.g., skis, poles) alongside anatomical landmarks. Continuous frame-wise calibration and 3D reconstruction were performed using Direct Linear Transformation. Evaluation compared automated detections with manual annotations. Automated reference point detection achieved a mean localization error of 4.1 pixels (0.1% of 4K width) and reduced 3D segment-length variation by 23%. The skier-specific keypoint model reached 98% PCK, mAP of 0.97, and an MPJPE of 10.3 pixels while lowering 3D segment-length variation by 0.5 cm compared to manual digitization and 0.6 cm relative to a pretrained model. Replacing manual digitization with automated detection improves accuracy and facilitates kinematic data collection in large outdoor fields with many athletes and trials. The approach also enables the creation of sport-specific datasets valuable for biomechanical research and training next-generation 3D pose estimation models. Full article
(This article belongs to the Special Issue Advanced Sensors in Biomechanics and Rehabilitation—2nd Edition)
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15 pages, 3170 KB  
Article
Measuring Relative Component Motion and Stability in Total Hip Replacements Using a Magnetic Position and Orientation Sensing System
by Oliver G. Vickers, Peter R. Culmer, Graham H. Isaac, Robert W. Kay, Matthew P. Shuttleworth, Tim N. Board and Sophie Williams
Sensors 2025, 25(23), 7280; https://doi.org/10.3390/s25237280 - 29 Nov 2025
Viewed by 885
Abstract
An instrumented total hip replacement (THR) implant capable of remote and continuous monitoring would be an attractive prospect for a surgeon to conveniently track the recovery of their patients. Measuring the relative motion of the prosthesis components would provide insight into joint kinematics [...] Read more.
An instrumented total hip replacement (THR) implant capable of remote and continuous monitoring would be an attractive prospect for a surgeon to conveniently track the recovery of their patients. Measuring the relative motion of the prosthesis components would provide insight into joint kinematics and contribute to the detection of adverse events including impingement and subluxation. The aim of this study was to develop a sensing system to measure the relative orientation and translation of the prosthesis components. A tri-axis magnetometer and a permanent magnet were integrated into clinically available THR components, forming a magnetic position and orientation sensing system. A robotic arm was used to articulate the components through controlled motion routines and record the orientation of the components. The output of the robot arm and a camera tracking system were used to validate the performance of the sensing system. The sensing system measured the relative orientation of the components to two degrees of freedom with an RMSE of <4.0° and measured the displacement of the femoral head during an impingement-driven subluxation motion with an RMSE of 0.2 mm. This proof-of-concept work has shown that magnetic sensing technology can track the position and orientation of THR components. With further development, this sensing method could feature within an instrumented THR implant. Full article
(This article belongs to the Special Issue Advanced Sensors in Biomechanics and Rehabilitation—2nd Edition)
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