Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.7
2.5
Journals
JFMK
Special Issues
Applications of Machine Learning in Sports Medicine, Physical Activity, Posture,...
share announcement

Applications of Machine Learning in Sports Medicine, Physical Activity, Posture, and Rehabilitation: 2nd Edition

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: 31 July 2026 | Viewed by 6323

Special Issue Editor

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
Special Issues, Collections and Topics in MDPI journals

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
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. 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 1800 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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

12 pages, 1154 KB  
Article
The Role of Artificial Intelligence and Professional Expertise in Adapted Physical Activity Prescription for Orthopedic Rehabilitation
by Martina Sortino, Bruno Trovato, Rita Chiaramonte, Antonio Carrera, Marco Sapienza, Federico Roggio and Giuseppe Musumeci
J. Funct. Morphol. Kinesiol. 2026, 11(1), 113; https://doi.org/10.3390/jfmk11010113 - 9 Mar 2026
Viewed by 624
Abstract
Background: Adapted Physical Activity (APA) prescription is a complex decision-making process that integrates clinical guidelines and individual patient characteristics and remains strongly dependent on clinician experience. Generative artificial intelligence (AI) has recently emerged as a potential decision-support tool in exercise prescription; however, [...] Read more.
Background: Adapted Physical Activity (APA) prescription is a complex decision-making process that integrates clinical guidelines and individual patient characteristics and remains strongly dependent on clinician experience. Generative artificial intelligence (AI) has recently emerged as a potential decision-support tool in exercise prescription; however, its interaction with professional expertise is still unclear. This study compared the perceived quality of APA protocols developed by expert professionals, novice professionals supported by AI, and AI operating autonomously across multiple orthopedic conditions. Methods: In this observational cross-sectional study, five real orthopedic prescriptions (scoliosis, low back pain, osteoporosis, high risk of falls, and osteoarthritis) were used to generate three APA protocols per condition: expert professional (EP), novice professional with AI support (NAI), and AI alone. All protocols were created using an identical standardized prompt and anonymized. A multidisciplinary panel of 135 professionals blindly evaluated the protocols using a structured questionnaire assessing effectiveness, safety, appropriateness, clarity, and progression. Overall quality scores were compared using Friedman tests with post hoc Wilcoxon signed-rank tests. Results: Across all conditions, EP protocols achieved the highest quality scores, followed by NAI, while AI-alone protocols consistently received the lowest ratings (all p < 0.05). NAI protocols showed intermediate performance, partially reducing the expertise gap. Post hoc analyses showed that EP protocols received significantly higher rating than AI protocols in all conditions (p < 0.01). NAI protocols received significantly higher rating than AI protocols in most conditions (p < 0.01) except osteoporosis (p = 0.362). Differences between EP and AI were most pronounced for safety (p < 0.01), appropriateness (tailoring p < 0.01), and progression (p < 0.05), whereas EP–NAI differences were smaller and condition-dependent. AI-alone protocols showed greater variability across pathologies. Conclusions: Professional expertise remains the main determinant of APA protocol quality. AI support can improve protocol structure and perceived quality when used by novice professionals but does not replace expert clinical reasoning. AI-generated protocols without human oversight are not yet suitable for autonomous APA prescription, supporting a complementary, expertise-dependent role of AI in exercise programming. Full article
(This article belongs to the Special Issue Applications of Machine Learning in Sports Medicine, Physical Activity, Posture, and Rehabilitation: 2nd Edition)
►▼ Show Figures

Figure 1

18 pages, 308 KB  
Article
Can We Predict Adductor Strain? A Predictive Analysis of a Major League Soccer (MLS) Cohort Spanning from 2019 to 2022
by Rebecca Davis, Benjamin C. Brewer, Martha Hall and Jill S. Higginson
J. Funct. Morphol. Kinesiol. 2026, 11(1), 108; https://doi.org/10.3390/jfmk11010108 - 5 Mar 2026
Viewed by 530
Abstract
Background: Despite the high prevalence of adductor injury in soccer, there is limited injury-specific predictive modeling to identify common risk factors. The objective of this study was to create an adductor strain prediction model utilizing injury, game, and performance data collected from a [...] Read more.
Background: Despite the high prevalence of adductor injury in soccer, there is limited injury-specific predictive modeling to identify common risk factors. The objective of this study was to create an adductor strain prediction model utilizing injury, game, and performance data collected from a cohort of professional Major League Soccer (MLS) players. Methods: We identified potential risk factors for soft tissue, non-contact adductor strain using a predictive machine learning model framework. Performance and injury data were collected between the 2019 to 2022 seasons of one professional MLS team. We utilized Random Forest (RF) machine learning models with Synthetic Minority Oversampling (SMOTE) to predict soft tissue, non-contact adductor strain injury amongst the cohort. Features chosen to be implemented in the model included injury, game, and performance data. Results: From the four models constructed in this study, the best performing model included Catapult Global Position System (GPS)/Internal Measurement Unit (IMU), strength, injury, and game data using a weekly structure determined by F1 score. Multiple models indicated that not having a previous injury lowers the odds of a future injury in the following week or month. Forwards had greater odds of injury whereas defenders had lower odds of injury. Greater hamstring max force lowered odds of injury whereas a greater amount of change of direction efforts increased the odds of injury in the following week or month. Adductor-to-abductor max strength ratio showed conflicting results regarding the odds of future injury. Conclusions: Through the utilization of RF and SMOTE, we were able to successfully predict adductor injuries in an MLS cohort utilizing injury, game, and performance metrics. Validation in a larger cohort would be highly recommended before utilizing the findings of this study in the design of injury prevention protocols. Full article
(This article belongs to the Special Issue Applications of Machine Learning in Sports Medicine, Physical Activity, Posture, and Rehabilitation: 2nd Edition)

Review

Jump to: Research

39 pages, 2426 KB  
Review
Machine Learning in Adapted Physical Activity: Clinical Applications, Monitoring, and Implementation Pathways for Personalized Exercise in Chronic Conditions: A Narrative Review
by Gianpiero Greco, Alessandro Petrelli, Luca Poli, Francesco Fischetti and Stefania Cataldi
J. Funct. Morphol. Kinesiol. 2026, 11(1), 106; https://doi.org/10.3390/jfmk11010106 - 4 Mar 2026
Viewed by 986
Abstract
Machine learning (ML) is increasingly influencing the assessment and delivery of movement and exercise, yet its role within adapted physical activity (APA) for individuals with chronic conditions has not been comprehensively synthesized. ML-based approaches have the potential to enhance functional assessment, support individualized [...] Read more.
Machine learning (ML) is increasingly influencing the assessment and delivery of movement and exercise, yet its role within adapted physical activity (APA) for individuals with chronic conditions has not been comprehensively synthesized. ML-based approaches have the potential to enhance functional assessment, support individualized exercise prescription, and facilitate scalable monitoring across preventive, community-based, and long-term adapted exercise settings, particularly in populations characterized by functional heterogeneity and variable responses to exercise. The aim of this narrative review is to synthesize and critically discuss current ML applications relevant to the core professional processes of APA practice. A structured narrative review was conducted using searches in PubMed/MEDLINE, Scopus, and Web of Science, complemented by targeted searches in engineering-oriented sources to capture ML methods not consistently indexed in biomedical databases. The search covered the period in which contemporary ML approaches have been increasingly applied to human movement and exercise research and was last updated in January 2026. Evidence was synthesized thematically into application-oriented domains relevant to APA practice. ML applications in APA include markerless motion and gait analysis, wearable-sensor data processing, balance and fall-risk assessment, and functional classification. Predictive and adaptive models support individualized regulation of exercise intensity, progression, and workload, including remote and hybrid delivery models. Applications span oncology, cardiometabolic, respiratory, neuromuscular conditions, and adapted sport contexts. Ethical, legal, and governance issues, such as algorithmic bias, data privacy, and professional accountability, emerge as central considerations for safe and equitable implementation. ML represents a promising decision-support layer for APA, complementing professional expertise through enhanced assessment, personalization, and monitoring. Its effective integration requires robust validation, interpretability, and responsible governance to ensure that ML augments, rather than replaces, professional judgment in APA practice. Full article
(This article belongs to the Special Issue Applications of Machine Learning in Sports Medicine, Physical Activity, Posture, and Rehabilitation: 2nd Edition)
►▼ Show Figures

Figure 1

12 pages, 452 KB  
Review
Telerehabilitation in Hip and Knee Arthroplasty: A Narrative Review of Clinical Outcomes, Patient-Reported Measures, and Implementation Challenges
by Rocco Maria Comodo, Daniele Grassa, Alessandro El Motassime, Guido Bocchino, Riccardo Totti, Andrea De Fazio, Cesare Meschini, Giacomo Capece, Giulio Maccauro and Raffaele Vitiello
J. Funct. Morphol. Kinesiol. 2025, 10(4), 370; https://doi.org/10.3390/jfmk10040370 - 26 Sep 2025
Cited by 5 | Viewed by 3526
Abstract
Background: Total hip and knee arthroplasty are common procedures for end-stage osteoarthritis, with rehabilitation playing a central role in functional recovery. Conventional face-to-face programs are often limited by accessibility, costs, and logistical barriers. Digital telerehabilitation has been increasingly investigated as an alternative. [...] Read more.
Background: Total hip and knee arthroplasty are common procedures for end-stage osteoarthritis, with rehabilitation playing a central role in functional recovery. Conventional face-to-face programs are often limited by accessibility, costs, and logistical barriers. Digital telerehabilitation has been increasingly investigated as an alternative. This review aims to summarize current evidence on its effectiveness, patient-reported outcomes, satisfaction, and economic impact. Materials and Methods: A narrative review was conducted using Medline, Web of Science, and Scopus up to April 2025. Randomized controlled trials and longitudinal studies evaluating telerehabilitation after total hip or knee arthroplasty were included. Data were extracted on functional performance, pain, autonomy, quality of life, patient satisfaction, and cost-effectiveness. Results: Across multiple RCTs, telerehabilitation produced functional outcomes generally comparable to conventional rehabilitation, with some studies reporting superior short-term improvements. For example, in a retrospective trial, Timed Up and Go improved by −8.0 ± 2.6 s in the digital group versus −4.9 ± 2.5 s with standard care (p < 0.01). Tablet-assisted programs reduced Five Times Sit-to-Stand times to 11.7 s at 6 months compared with 14.7 s in controls (p = 0.05). In hip arthroplasty, digital rehabilitation resulted in higher active flexion (97.4° vs. 89.9°, p = 0.018) and abduction (51.7° vs. 43.8°, p = 0.024). Quality-of-life measures, such as EQ-5D VAS, also showed improvements (82.9 ± 4.3 vs. 68.7 ± 4.6 at 6 months). Some studies reported higher patient satisfaction, for instance, a VR-based RCT found GPE at day 15 of 4.76 ± 0.43 in the intervention group versus 3.96 ± 0.65 in controls (p < 0.001). Conclusions: Telerehabilitation after hip and knee arthroplasty appears to produce short-term functional and patient-reported outcomes comparable to conventional rehabilitation in selected populations. Evidence of superiority is limited and heterogeneous, and long-term effectiveness, equity, and cost-effectiveness remain uncertain. Heterogeneity in protocols and digital literacy barriers highlight the need for standardized guidelines and further independent validation. Full article
(This article belongs to the Special Issue Applications of Machine Learning in Sports Medicine, Physical Activity, Posture, and Rehabilitation: 2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop