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Advances in Sport and Exercise Biomechanics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 715

Special Issue Editors


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Guest Editor
School of Sport, Health and Exercise Science, Spinnaker Building, University of Portsmouth, Portsmouth, UK
Interests: musculoskeletal modeling; soft tissue dynamics; novel technologies and methods; ergonomic sports product design; bio-feedback

E-Mail Website
Guest Editor
School of Sport, Health and Exercise Science, Spinnaker Building, University of Portsmouth, Portsmouth, UK
Interests: sports biomechanics; novel technologies and methodological approaches; applied biomechanics; in-field testing; real-time feedback

Special Issue Information

Dear Colleagues,

In recent years, the field of sport and exercise biomechanics has experienced significant advancements, driven by cutting-edge technology and innovative research methodologies. Advanced motion capture systems, wearable sensors, and sophisticated modeling software have revolutionized our ability to analyze and interpret human movement with unprecedented precision. These tools have facilitated deeper insight into the intricacies of movement patterns, muscle activation, and force generation. Moreover, the application of advanced computational models and machine learning algorithms provides deeper insights into complex biomechanical phenomena. By simulating various scenarios and predicting outcomes, these models assist in understanding technique execution and interactions with equipment and the environment.

The featured studies highlight the transformative impact of these advancements, showcasing how contemporary biomechanics research is enhancing our understanding of human movement and contributing to the fields of sports science, physical therapy, and health and exercise promotion.

Dr. Chris Mills
Dr. Timothy A. Exell
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.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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

  • simulation
  • machine learning
  • artificial intelligence
  • wearable technology
  • ecological based research
  • medicine
  • accessible technology
  • sport performance
  • injury rehabilitation
  • health

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Published Papers (1 paper)

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Research

13 pages, 2338 KiB  
Article
Investigating the Effect of Motion Capture Suits on the Test–Retest Reliability of Gait Parameters
by Matt C. Smith, Phaedra Leveridge, Garry Massey, Jessica Tyrrell, Malcolm Hilton and Genevieve K. R. Williams
Appl. Sci. 2024, 14(18), 8570; https://doi.org/10.3390/app14188570 - 23 Sep 2024
Viewed by 498
Abstract
When collecting marker-based motion capture data from clinical populations, speed of collection and comfort for the participant is a priority. This could be achieved by attaching markers to motion capture Velcro suits, as opposed to the skin. This study aimed to ascertain the [...] Read more.
When collecting marker-based motion capture data from clinical populations, speed of collection and comfort for the participant is a priority. This could be achieved by attaching markers to motion capture Velcro suits, as opposed to the skin. This study aimed to ascertain the reliability of sagittal-plane gait parameters estimated using Plug-in Gait (PiG) and Conventional Gait Model 2 (CGM2) marker sets from data collected in Suited and Non-suited (markers placed onto skin) conditions. For ten participants, markers were placed based on PiG and CGM2 models and data captured during a 2-min treadmill walk. Trials were repeated in suited and non-suited conditions. PiG ankle flexion/extension measurements had poor/moderate reliability (Non-suited ICC = 0.531, Suited ICC = 0.435). CGM2 ankle flexion/extension measurements had good/excellent reliability (Non-suited ICC = 0.916, Suited ICC = 0.900). There were significant differences in minimal detectable change (MDC) between conditions at the ankle for PiG (Non-suited MDC = 2.32°, Suited MDC = 18.90°), but not for CGM2 (Non-suited MDC = 0.63°, Suited MDC = 0.95°). When using CGM2, knee (Non-suited ICC = 0.878, Suited ICC = 0.855) and hip (Non-suited ICC = 0.897, Suited ICC = 0.948) showed good/excellent reliability in both conditions. A motion capture suit is not a reliable solution when collecting joint angle data using the PiG model but is reliable enough to consider when using the CGM2 model. Full article
(This article belongs to the Special Issue Advances in Sport and Exercise Biomechanics)
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