Injury Prevention and Rehabilitation Using Machine Learning for Athletes

Yadav, Mohit; Choudhury, Tanupriya; Huzooree, Geshwaree

Abstract

This chapter explores the role of machine learning (ML) in injury prevention and rehabilitation for athletes. It examines how ML models can predict injuries by analysing diverse data sources, such as biomechanics, wearables, and medical records, and highlights the potential for personalized, data-driven injury prevention strategies. The chapter also addresses how AI-driven rehabilitation programs can adapt in real-time to optimize recovery and reduce the risk of re-injury. Key challenges, such as data privacy, model complexity, and the need for explainable AI, are discussed, along with future trends like the integration of wearable technology, federated learning, and virtual reality in rehabilitation. These innovations promise to transform sports medicine by making injury prevention more accurate and rehabilitation more efficient, ultimately enhancing athlete performance and longevity.

Citation

Yadav, M., Choudhury, T., & Huzooree, G. (2025). Injury Prevention and Rehabilitation Using Machine Learning for Athletes. In T. Choudhury, P. Kumar Arya, K. Kotecha, A. Sharma, & J.-S. Um (Eds.), AI and Machine Learning Applications in Sports Analytics (129-156). IGI Global. https://doi.org/10.4018/979-8-3693-5385-1.ch007

• Online Publication Date: May 30, 2025
• Publication Date: May 30, 2025
• Deposit Date: Jun 2, 2025
• Publisher: IGI Global
• Pages: 129-156
• Book Title: AI and Machine Learning Applications in Sports Analytics
• ISBN: 9798369353851
• DOI: https://doi.org/10.4018/979-8-3693-5385-1.ch007
• Public URL: http://researchrepository.napier.ac.uk/Output/4521912