Mittal, Shashank, Chandel, Ajay
and Ta, Huy Hung
(2025)
AI-Based Player Fatigue and Workload Monitoring Systems.
In:
AI and Machine Learning Applications in Sports Analytics.
IGI Global, pp. 15-39.
ISBN 9798369353851
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Abstract
The Present chapter examines AI-based player fatigue and workload monitoring systems in optimizing the performance of athletes while preventing them from burning out. These are harnessed from advanced models of machine learning, analyzing physiological, biomechanical, and mental health data to improve training and recovery strategies. Advances in wearable technologies will dramatically change the way coaches and athletes approach the world of performance management by harnessed real-time data and predictive analytics. This chapter also deals with some of the difficulties, including data accuracy, privacy and ethical issues, associated with athlete autonomy. Other possible future trends relate to cognitive metrics, multisport data sharing, and involvement of virtual or augmented reality in training environments. The goal at the end of the road for AI-based systems is optimal athletic performance, but all this should be done with the aim of supporting long-term health and well-being.
Item Type: | Book Section |
---|---|
Subjects: | Social Sciences and humanities > Business, Management and Accounting > Management of Technology and Innovation Physical, Life and Health Sciences > Computer Science |
JGU School/Centre: | Jindal Global Business School |
Depositing User: | Mr. Luckey Pathan |
Date Deposited: | 08 Jul 2025 10:09 |
Last Modified: | 08 Jul 2025 10:09 |
Official URL: | https://www.igi-global.com/chapter/ai-based-player... |
URI: | https://pure.jgu.edu.in/id/eprint/9795 |
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