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.

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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
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article

Relationship between training load and recovery rate in artificial intelligence-based sports rehabilitation training

Yuan Chen, Tong Chen, Yue Xian
Molecular & cellular biomechanics
AI and Big Data Applications
article

Relationship between training load and recovery rate in artificial intelligence-based sports rehabilitation training

Yuan Chen, Tong Chen, Yue Xian
article en

Abstract

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.

Molecular & cellular biomechanicsVol. 22(4)
Beijing Normal University (CN), Jining Traditional Chinese Medicine Hospital (CN)
Openalex Percentile: Top 94%
AI and Big Data Applications
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