@inproceedings{b2fd5d1b483242bfbcfb39683978cf34,
title = "Sports fatigue detection based on deep learning",
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.",
keywords = "Bi-LSTM, Deep learning, ECG, HRV, Sports fatigue recognition, acceleration velocity, angular velocity",
author = "Xiaole Guan and Yanfei Lin and Qun Wang and Zhiwen Liu and Chengyi Liu",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021 ; Conference date: 23-10-2021 Through 25-10-2021",
year = "2021",
doi = "10.1109/CISP-BMEI53629.2021.9624395",
language = "English",
series = "Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Qingli Li and Lipo Wang and Yan Wang and Wenwu Li",
booktitle = "Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021",
address = "United States",
}
