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 January 2026 | Viewed by 1462

Special Issue Editor


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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
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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

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Keywords

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

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Published Papers (1 paper)

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Review

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
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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
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