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Applications of Machine Learning in Sports Medicine, Physical Activity, Posture,...
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Applications of Machine Learning in Sports Medicine, Physical Activity, Posture, and Rehabilitation

A special issue of Journal of Functional Morphology and Kinesiology (ISSN 2411-5142). This special issue belongs to the section "Physical Exercise for Health Promotion".

Deadline for manuscript submissions: 30 November 2024 | Viewed by 1482

Special Issue Editors

Dr. Federico Roggio

E-Mail Website
Guest Editor
Department of Biomedical and Biotechnological Sciences, Section of Anatomy, Histology and Movement Science, School of Medicine, University of Catania, Via S. Sofia 97, 95123 Catania, Italy
Interests: movement analysis; motion capture; posture; kinesiology; gait analysis; posture screening; rasterstereography; musculoskeletal disorders; low back pain; scoliosis
Dr. Mark Elliott

E-Mail Website
Guest Editor
School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham B15 2TT, UK
Interests: human movement analysis; behaviour change; digital incentives; sensory integration; time series analysis; neural networks; machine learning

Special Issue Information

Dear Colleagues,

Recent and innovative advancements in artificial intelligence are transforming the way we approach sports medicine, physical activity, posture, and rehabilitation, leading to improved performance, health outcomes, and quality of life. This progress has opened new frontiers of research and clinical applications, promoting an evolution in sports performance, physical health, and the quality of life of patients.

Artificial intelligence, specifically machine learning, is revolutionizing biomechanical analyses, allowing for the improvement of sports skills and a reduction in the risk of injury. Thanks to specific algorithms, it supports physical activity programs adapted to individual health goals, promoting active, healthy lifestyles. It is supporting remote patient care, enabling effective and real-time services outside of urban centers. In addition, machine learning models based on data from wearable devices allow for accurate assessments of rehabilitation outcomes, personalizing treatment and facilitating a gradual return to physical activity and competition. The advent of human pose estimation models allowed for a detailed assessment of human posture, detecting and analyzing the position of different body parts. This approach is essential for identifying any postural imbalances or technical errors during exercise performance, thus helping to prevent injuries, improve performance, and provide real-time feedback during exercise or rehabilitation. The application of machine learning represents a valuable innovation in sports and health, improving performance, promoting an active lifestyle, and facilitating rehabilitation. However, as an extremely popular and studied research field, in recent years, it is important to be aware of its real applications in the field of human movement.

This Special Issue aims to explore the broad applications of machine learning in sports medicine, exercise, posture, and rehabilitation. We seek contributions ranging from original research to systematic reviews, with a particular focus on biomechanical analysis, personalized exercise plans, rehabilitation, and injury prevention. Additionally, this Special Issue will carefully highlight the real applications of these models, along with their strengths, limitations, and future challenges for their integration in the context of sports.

Dr. Federico Roggio
Dr. Mark Elliott
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. Journal of Functional Morphology and Kinesiology is an international peer-reviewed open access quarterly 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 1600 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

  • machine learning
  • artificial intelligence
  • sports medicine
  • physical activity
  • posture
  • rehabilitation
  • biomechanics
  • injury prevention
  • human pose estimation

Published Papers (1 paper)

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Research

12 pages, 1745 KiB  
Article
Data Mining Paths for Standard Weekly Training Load in Sub-Elite Young Football Players: A Machine Learning Approach
by José E. Teixeira, Samuel Encarnação, Luís Branquinho, Ryland Morgans, Pedro Afonso, João Rocha, Francisco Graça, Tiago M. Barbosa, António M. Monteiro, Ricardo Ferraz and Pedro Forte
J. Funct. Morphol. Kinesiol. 2024, 9(3), 114; https://doi.org/10.3390/jfmk9030114 - 28 Jun 2024
Cited by 1 | Viewed by 1159
Abstract
The aim of this study was to test a machine learning (ML) model to predict high-intensity actions and body impacts during youth football training. Sixty under-15, -17, and -19 sub-elite Portuguese football players were monitored over a 6-week period. External training load data [...] Read more.
The aim of this study was to test a machine learning (ML) model to predict high-intensity actions and body impacts during youth football training. Sixty under-15, -17, and -19 sub-elite Portuguese football players were monitored over a 6-week period. External training load data were collected from the target variables of accelerations (ACCs), decelerations (DECs), and dynamic stress load (DSL) using an 18 Hz global positioning system (GPS). Additionally, we monitored the perceived exertion and biological characteristics using total quality recovery (TQR), rating of perceived exertion (RPE), session RPE (sRPE), chronological age, maturation offset (MO), and age at peak height velocity (APHV). The ML model was computed by a feature selection process with a linear regression forecast and bootstrap method. The predictive analysis revealed that the players’ MO demonstrated varying degrees of effectiveness in predicting their DEC and ACC across different ranges of IQR. After predictive analysis, the following performance values were observed: DEC (x¯predicted = 41, β = 3.24, intercept = 37.0), lower IQR (IQRpredicted = 36.6, β = 3.24, intercept = 37.0), and upper IQR (IQRpredicted = 46 decelerations, β = 3.24, intercept = 37.0). The player’s MO also demonstrated the ability to predict their upper IQR (IQRpredicted = 51, β = 3.8, intercept = 40.62), lower IQR (IQRpredicted = 40, β = 3.8, intercept = 40.62), and ACC (x¯predicted = 46 accelerations, β = 3.8, intercept = 40.62). The ML model showed poor performance in predicting the players’ ACC and DEC using MO (MSE = 2.47–4.76; RMSE = 1.57–2.18: R2 = −0.78–0.02). Maturational concerns are prevalent in football performance and should be regularly checked, as the current ML model treated MO as the sole variable for ACC, DEC, and DSL. Applying ML models to assess automated tracking data can be an effective strategy, particularly in the context of forecasting peak ACC, DEC, and bodily effects in sub-elite youth football training. Full article
(This article belongs to the Special Issue Applications of Machine Learning in Sports Medicine, Physical Activity, Posture, and Rehabilitation)
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