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Bioengineering
Special Issues
Biomechanics in Sport and Motion Analysis
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Biomechanics in Sport and Motion Analysis

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomechanics and Sports Medicine".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 1785

Special Issue Editors

Prof. Dr. Kamran Iqbal

E-Mail Website
Guest Editor
Department of Systems Engineering, University of Arkansas Little Rock, Little Rock, AR 72204, USA
Interests: sensorimotor control of human movement; biomedical signal and image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Rajat Emanuel Singh

E-Mail Website
Guest Editor
Department of Kinesiology, Northwestern College, Orange City, IA 51041, USA
Interests: biomechanics; neural engineering; motor control; motor learning; electromyography; motion capture; kinesiology

Special Issue Information

Dear Colleagues,

Technological advancements, including motion capture, electromyography, inertial measurement units, and force plates, have significantly enhanced our ability to capture variables associated with the laws of physics that govern movement during sports-related activities. Furthermore, these physical variables can be simulated for prediction and/or modeled with a proper artificial intelligence (AI) system, leading to enhanced performance or improved foul detection during sports activities and events. Therefore, in this Special Issue, we aim to collect relevant theories, methods, mathematical models, simulations, and clinical results that advance our understanding of biomechanical systems in the form of original research articles, review articles, short articles, and opinions applied in the fields of biomechanics and sports medicine.

We welcome contributions from biomedical engineers, biomechanists, sports scientists, neuroscientists, physical therapists, and other practitioners.

The major topics of interest for this Special Issue include (but are not limited to) the following:

  1. Biomedical signal processing;
  2. Biomechanics and sports medicine;
  3. Motor control and motor learning;
  4. Artificial intelligence and bioinformatics;
  5. Biomedical signal and image processing;
  6. Biomechanical modeling and simulation;
  7. Motion and data capture;
  8. Electromyography;
  9. EMG-driven models;
  10. Muscle synergies.

Prof. Dr. Kamran Iqbal
Dr. Rajat Emanuel Singh
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. Bioengineering is an international peer-reviewed open access monthly 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 2700 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
  • sports science
  • human motion
  • artificial intelligence

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Published Papers (2 papers)

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12 pages, 3764 KiB  
Article
Estimation of Three-Dimensional Ground Reaction Force and Center of Pressure During Walking Using a Machine-Learning-Based Markerless Motion Capture System
by Ru Feng, Ukadike Christopher Ugbolue, Chen Yang and Hui Liu
Bioengineering 2025, 12(6), 588; https://doi.org/10.3390/bioengineering12060588 - 29 May 2025
Viewed by 289
Abstract
Objective: We developed two neural network models to estimate the three-dimensional ground reaction force (GRF) and center of pressure (COP) based on marker trajectories obtained from a markerless motion capture system. Methods: Gait data were collected using two cameras and three force plates. [...] Read more.
Objective: We developed two neural network models to estimate the three-dimensional ground reaction force (GRF) and center of pressure (COP) based on marker trajectories obtained from a markerless motion capture system. Methods: Gait data were collected using two cameras and three force plates. Each gait dataset contained kinematic data and kinetic data from the stance phase. A multi-layer perceptron (MLP) and convolutional neural network (CNN) were constructed to estimate each component of GRF and COP based on the three-dimensional trajectories of the markers. A total of 100 samples were randomly selected as the test set, and the estimation performance was evaluated using the correlation coefficient (r) and relative root mean square error (rRMSE). Results: The r-values for MLP in each GRF component ranged from 0.918 to 0.989, with rRMSEs between 5.06% and 12.08%. The r-values for CNN in each GRF component ranged from 0.956 to 0.988, with rRMSEs between 6.03–9.44%. For the COP estimation, the r-values for MLP ranged from 0.727 to 0.982, with rRMSEs between 6.43% and 27.64%, while the r-values for CNN ranged from 0.896 to 0.977, with rRMSEs between 6.41% and 7.90%. Conclusions: It is possible to estimate GRF and COP from markerless motion capture data. This approach provides an alternative method for measuring kinetic parameters without force plates during gait analysis. Full article
(This article belongs to the Special Issue Biomechanics in Sport and Motion Analysis)
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15 pages, 3916 KiB  
Article
Does the Fatigue Induced by a 30-Minute Run Affect the Lower Limb Acceleration Spikes’ Asymmetries?
by Gabriel Delgado-García, Isabel M. Martín-López, Fulgencio Soto-Méndez, Arturo Quílez-Maimón and Salvador Boned-Gómez
Bioengineering 2025, 12(3), 294; https://doi.org/10.3390/bioengineering12030294 - 14 Mar 2025
Viewed by 528
Abstract
Running-induced fatigue affects several biomechanical parameters, and yet few studies are focused on the acceleration spikes’ asymmetries. This study aimed to evaluate the effects of a 30 min run on lower limbs spikes’ asymmetries. Eighteen recreational runners (35.6 ± 7.5 years; seven women) [...] Read more.
Running-induced fatigue affects several biomechanical parameters, and yet few studies are focused on the acceleration spikes’ asymmetries. This study aimed to evaluate the effects of a 30 min run on lower limbs spikes’ asymmetries. Eighteen recreational runners (35.6 ± 7.5 years; seven women) performed a treadmill running protocol at a moderate speed and acceleration spikes’ asymmetries and kinematic (temporal) parameters were measured via accelerometers—on the tibias and sacrum—and photogrammetry. Acceleration spikes’ parameters were continuously measured and averaged per minute to assess the relationship between fatigue and acceleration spike asymmetries via a linear regression model. Right tibial acceleration spikes increased over time (r = 0.9; p < 0.001) and left tibia spikes decreased (r = 0.78; p < 0.001), with a rise in tibial load asymmetry from 9% to 25% at the end (r = 0.98; p < 0.001). This study suggest that fatigue affects the acceleration spikes of the two legs differently, with increasingly greater acceleration spikes in the right (dominant) leg. These findings should be considered, as greater asymmetries are related to overuse injuries and lower efficiency. Also, in studies focusing on running mechanics with fatigue, it is recommended that researchers collect data from both limbs, and not only from the right (dominant) leg. Full article
(This article belongs to the Special Issue Biomechanics in Sport and Motion Analysis)
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