Sports fatigue detection based on deep learning

2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2021-10-23 DOI:10.1109/CISP-BMEI53629.2021.9624395

Xiaole Guan, Yanfei Lin, Qun Wang, Zhiwen Liu, Cheng-Shui Liu

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引用次数: 10

Abstract

Moderate exercise is good for human health. However, when the exercise intensity exceeds a certain level, it will be harmful to the human body. Therefore, precise control and adjustment of exercise load can ensure athletes' sports safety and improve their competitive performance. In this work, we have developed wearable exercise fatigue detection technology to estimate the human body's exercise fatigue state using real-time monitoring of the ECG signal and Inertial sensor signal of the human body. 14 young healthy volunteers participated in the running experiment, wearing ECG acquisition equipment and inertial sensors. ECG, acceleration and angular velocity signals were collected to extract features. And then Bidirectional long and short-term memory neural network (Bi-LSTM) was used to classify three levels of sports fatigue. The results showed that the recognition accuracy of the user-independent model was 80.55%. The experimental results verified the effectiveness of the algorithm.

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适度运动对人体健康有益。但是，当运动强度超过一定水平时，就会对人体产生危害。因此，精确控制和调整运动负荷可以保证运动员的运动安全，提高运动员的竞技成绩。在这项工作中，我们开发了可穿戴运动疲劳检测技术，通过实时监测人体的心电信号和惯性传感器信号来估计人体的运动疲劳状态。14名年轻健康志愿者参加跑步实验，佩戴心电采集设备和惯性传感器。采集心电、加速度和角速度信号提取特征。然后采用双向长短期记忆神经网络(Bi-LSTM)对运动疲劳的三个程度进行分类。结果表明，用户独立模型的识别准确率为80.55%。实验结果验证了该算法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

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