Biomechanics 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 September 2024 | Viewed by 1054

Special Issue Editors


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Guest Editor
Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: aging; skeletal muscle; balance and gait; prevention of falls and slips; physical activity; smart wearable technology
Special Issues, Collections and Topics in MDPI journals
Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong, China
Interests: computer vision algorithms; artificial intelligence in healthcare and education; eye tracking and motion detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Research in the field of biomechanics and motion analysis has seen rapid growth in recent years. Conducting biomechanical and motion analyses can build on our knowledge and understanding regarding normal human posture and locomotion, pathological movement, physical disorders, and how humans interact with the environment biomechanically, as well as facilitate the development of targeted sport and rehabilitation approaches, etc. With the recent advancements in technology, it is also feasible for researchers to apply a number of state-of-the-art systems, devices, and algorithms with which to study biomechanics and motion, from external body movement to internal cellular responses, from indoor lab settings to the outdoor environment, and from bench experiments to clinical applications.

This Special Issue will focus on recent research and developments in biomechanics and motion analysis.

The journal will be accepting contributions (both original articles and reviews) that mainly focus on the following topics:

  • Biomechanics;
  • Upper-limb biomechanics;
  • Lower-limb biomechanics;
  • Spinal biomechanics;
  • Motion capture and analysis;
  • Interface biomechanics;
  • Shear force in ergonomics;
  • Pressure in ergonomics;
  • Computational orthopedics;
  • Physical ergonomics;
  • Sports engineering;
  • Rehabilitation engineering;
  • Biomechanical interaction between humans and environments;
  • Postural stability;
  • Balance and gait control;
  • Physical activity;
  • Computer vision algorithms for motion capture and analysis;
  • Artificial intelligence (AI) in biomechanics;
  • Artificial intelligence (AI) in motion capture and analysis;
  • Wearable devices for biomechanical analysis.

Dr. Christina Zong-Hao Ma
Dr. Hong Fu
Guest Editors

Manuscript Submission Information

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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
  • motion capture and analysis
  • sports and rehabilitation engineering
  • artificial intelligence
  • assistive technology

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Published Papers (1 paper)

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Research

23 pages, 7915 KiB  
Article
Deep-Learning-Based Recovery of Missing Optical Marker Trajectories in 3D Motion Capture Systems
by Oleksandr Yuhai, Ahnryul Choi, Yubin Cho, Hyunggun Kim and Joung Hwan Mun
Bioengineering 2024, 11(6), 560; https://doi.org/10.3390/bioengineering11060560 - 1 Jun 2024
Viewed by 598
Abstract
Motion capture (MoCap) technology, essential for biomechanics and motion analysis, faces challenges from data loss due to occlusions and technical issues. Traditional recovery methods, based on inter-marker relationships or independent marker treatment, have limitations. This study introduces a novel U-net-inspired bi-directional long short-term [...] Read more.
Motion capture (MoCap) technology, essential for biomechanics and motion analysis, faces challenges from data loss due to occlusions and technical issues. Traditional recovery methods, based on inter-marker relationships or independent marker treatment, have limitations. This study introduces a novel U-net-inspired bi-directional long short-term memory (U-Bi-LSTM) autoencoder-based technique for recovering missing MoCap data across multi-camera setups. Leveraging multi-camera and triangulated 3D data, this method employs a sophisticated U-shaped deep learning structure with an adaptive Huber regression layer, enhancing outlier robustness and minimizing reconstruction errors, proving particularly beneficial for long-term data loss scenarios. Our approach surpasses traditional piecewise cubic spline and state-of-the-art sparse low rank methods, demonstrating statistically significant improvements in reconstruction error across various gap lengths and numbers. This research not only advances the technical capabilities of MoCap systems but also enriches the analytical tools available for biomechanical research, offering new possibilities for enhancing athletic performance, optimizing rehabilitation protocols, and developing personalized treatment plans based on precise biomechanical data. Full article
(This article belongs to the Special Issue Biomechanics and Motion Analysis)
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