Abstract
Fatigue monitoring is significant during movement process to avoid body injury cased by excessive exercise. To address this issue, we developed an automated framework to recognize human fatigue states based on electrocardiogram (ECG) collected by a smart wearable device. After preprocessing on the raw ECG data, both machine learning solution and deep learning solution were introduced to recognize the fatigue states. Specifically, a set of hand-crafted features were designed which are fed into different machine learning models for comparison. For the deep learning solution, the residual mechanism was employed to build a deep neural network for fatigue classification. The proposed methods were evaluated on data collected from subjects after running exercise and achieved an accuracy of \(89.54\%\).
This work was supported by the National Natural Science Foundation of China (Grant No. 61733011), Shanghai Key Clinical Disciplines Project and Guangdong Science and Technology Research Council (Grant No. 2020B1515120064).
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Liu, J., Zeng, J., Wang, Z., Liu, H. (2022). Modeling and Recognition of Movement-Inducing Fatigue State Based on ECG Signal. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13456. Springer, Cham. https://doi.org/10.1007/978-3-031-13822-5_61
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DOI: https://doi.org/10.1007/978-3-031-13822-5_61
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