Relationship between training load and recovery rate in artificial intelligence-based sports rehabilitation training
In the contemporary era characterized by rapid advancements in science and technology, artificial intelligence (AI) has emerged as a transformative force across various domains, particularly in the realm of sports rehabilitation training. Its unique advantages position AI as a pivotal element in enhancing training efficacy and facilitating recovery from injuries. The objective of this study is to thoroughly investigate the intricate relationship between training load and recovery rate within the context of AI-driven sports rehabilitation training, thereby offering robust theoretical foundations and practical guidance for the optimization of rehabilitation training programs. This paper commences with an overview of the latest developments in the application of AI in sports rehabilitation training, highlighting critical components such as data-driven monitoring of training loads, the formulation of personalized rehabilitation programs, and the assessment of recovery outcomes. Subsequently, the paper delves into a detailed analysis of the interaction mechanisms between training load and recovery rate, examining the impact of varying training loads on the recovery rates of both athletes and patients, as well as strategies for optimizing the recovery process through precise control of training loads. Finally, the paper emphasizes innovative strategies employed by artificial intelligence technology to adjust training loads and enhance recovery rates. This includes the utilization of machine learning algorithms for predicting individual recovery potential and the application of deep learning techniques for real-time monitoring and adaptive modification of training programs. Through this comprehensive investigation, the paper aspires to offer novel insights and methodologies for the scientific and individualized advancement of sports rehabilitation training, thereby contributing valuable knowledge and support to the swift recovery of athletes and patients.
Authors
- Yuan Chen (ORCID: https://orcid.org/0000-0002-2368-0198)
- Tong Chen (ORCID: https://orcid.org/0000-0002-0250-227X)
- Yue Xian (ORCID: https://orcid.org/0009-0005-3148-1382)
Institutions
- Beijing Normal University (CN)
- Jining Traditional Chinese Medicine Hospital (CN)
Publication Details
- Journal
- Molecular & cellular biomechanics
- Published
- 2025-03-19
- DOI
- https://doi.org/10.62617/mcb1419
- Primary Topic
- AI and Big Data Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00