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Design and application of blockchain and IoT-enabled sports injury rehabilitation monitoring system using neural network

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Abstract

Sports injuries have been a common concern during athlete training as the number of Chinese athletes and their training intensity have increased. These injuries frequently impact critical locations, including the waist, shoulder, and wrist, impeding training progress and intensity. Inadequate training levels, a lack of understanding of preventive measures, inappropriate movements, insufficient warm-up exercises, low physical condition, and an inability to adjust to the environment all contribute to sports injuries. As a result, athlete training and recovery suffer. To address these issues, this study presents a neural network-based sports injury risk analysis system that uses blockchain and the Internet of Things (IoT). The suggested system combines a multi-sensor data fusion technique based on blockchain and IoT to evaluate and analyze sports injuries completely. The system has three unique roles: coach, team doctor, and athlete, each with its functional modules. Sensors collect data on athletes’ recovery from sports injuries by collecting and analyzing inputs inside the human action system. The system then establishes the output values of a neural network and a risk level table, allowing for the estimation of athletes’ risk levels based on the given table. Comparing the traditional technique and the sports injury rehabilitation monitoring system demonstrates that the technology can reliably detect players’ injury locations in as little as 0.2 s. Furthermore, a thorough examination of the system’s tracking and analysis accuracy reveals a recovery rate of approximately 94.39%, demonstrating minimal fluctuations and improved stability. The experimental findings reveal that combining blockchain, IoT, and neural network technologies makes the proposed sports injury rehabilitation monitoring system useful in quickly recognizing damage positions and enabling accurate tracking and analysis.

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Data are available on reasonable request from the corresponding author.

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Authors and Affiliations

  1. P.E. Department, Hunan University of Finance and Economics, Changsha , 410205, Hunan, China

    Ning Li

  2. School of Physical Education, Hubei University of Science and Technology, Xianning, 437100, Hubei, China

    Xiaoyun Zhu

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  1. Ning Li
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  2. Xiaoyun Zhu
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Correspondence to Xiaoyun Zhu.

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Li, N., Zhu, X. Design and application of blockchain and IoT-enabled sports injury rehabilitation monitoring system using neural network. Soft Comput 27, 11815–11832 (2023). https://doi.org/10.1007/s00500-023-08677-w

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