<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This study helps athletes avoid and reduce the risk of injury in training more effectively by constructing a sports training injury risk assessment system to ensure they can train and compete safely and healthily. Based on the B/S model and .NET framework, this paper successfully develops a sports training injury risk assessment system and proposes a human exercise training detection program. The system integrates the measurement of physiological parameters such as blood oxygen saturation and blood pressure changes. It constructs a kinematic model to analyze the forces in training through inverse dynamics. In the system test, the response time was only 0.09ms/frame and the standby power consumption was as low as 11.43mW, demonstrating superior operational and energy efficiency. In addition, it was found that under specific conditions, such as after holding breath for 27.5s, the non-contact oximetry measurement showed a strong linear relationship with the physiological parameter detection module, which may predict the risk of falling when the peak motion acceleration SMV exceeds 3.23m<sup>2</sup>/s. Through this system, athletes can understand their body stress and physiological changes in the training process in real time, effectively avoiding potential training injuries, thus safeguarding their training safety and health.</p>
</abstract>

This study helps athletes avoid and reduce the risk of injury in training more effectively by constructing a sports training injury risk assessment system to ensure they can train and compete safely and healthily. Based on the B/S model and .NET framework, this paper successfully develops a sports training injury risk assessment system and proposes a human exercise training detection program. The system integrates the measurement of physiological parameters such as blood oxygen saturation and blood pressure changes. It constructs a kinematic model to analyze the forces in training through inverse dynamics. In the system test, the response time was only 0.09ms/frame and the standby power consumption was as low as 11.43mW, demonstrating superior operational and energy efficiency. In addition, it was found that under specific conditions, such as after holding breath for 27.5s, the non-contact oximetry measurement showed a strong linear relationship with the physiological parameter detection module, which may predict the risk of falling when the peak motion acceleration SMV exceeds 3.23m2/s. Through this system, athletes can understand their body stress and physiological changes in the training process in real time, effectively avoiding potential training injuries, thus safeguarding their training safety and health.

A Risk Assessment System for Sports Training Injuries Based on Artificial Intelligence and Big Data

Zhengzhou University of Economics and Business

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This study helps athletes avoid and reduce the risk of injury in training more effectively by constructing a sports training injury risk assessment system to...

A Risk Assessment System for Sports Training Injuries Based on Artificial Intelligence and Big Data

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 02. Jan. 2024

Akzeptiert: 09. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0343

Schlüsselwörter
.NET framework, Oxygen saturation, Inverse kinetics, Kinematic modeling, Risk assessment system, Sports training

© 2024 Fang Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A Risk Assessment System for Sports Training Injuries Based on Artificial Intelligence and Big Data

Online veröffentlicht: 26. Feb. 2024

Seitenbereich: -

Eingereicht: 02. Jan. 2024

Akzeptiert: 09. Jan. 2024

DOI: https://doi.org/10.2478/amns-2024-0343

Schlüsselwörter.NET framework, Oxygen saturation, Inverse kinetics, Kinematic modeling, Risk assessment system, Sports training

© 2024 Fang Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
.NET framework, Oxygen saturation, Inverse kinetics, Kinematic modeling, Risk assessment system, Sports training