Key result
A wearable exercise fatigue detection technology using ECG and inertial sensors with a Bi-LSTM neural network achieved a recognition accuracy of 80.55% for classifying sports fatigue.
Why the study?
Exercise exceeding a certain intensity is harmful, necessitating precise control of exercise load to ensure safety and performance.
Does wearable exercise fatigue detection technology using ECG and inertial sensors accurately classify sports fatigue in young healthy volunteers?
Does wearable exercise fatigue detection technology using ECG and inertial sensors accurately classify sports fatigue in young healthy volunteers?
A deep learning model using wearable ECG and inertial sensors can detect sports fatigue with 80.55% accuracy in healthy volunteers.
May aid real-time exercise monitoring; hypothesis-generating and requires larger validation before clinical adoption.
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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Guan et al. (2021) studied Sports fatigue (n=14). Wearable exercise fatigue detection technology using ECG and inertial sensors with Bi-LSTM was evaluated on Recognition accuracy of the user-independent model for classifying three levels of sports fatigue. A wearable exercise fatigue detection technology using ECG and inertial sensors with a Bi-LSTM neural network achieved a recognition accuracy of 80.55% for classifying sports fatigue.
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