@inproceedings{c1189cd163884bb6b3d6f9f9a443f718,
title = "Modeling and Recognition of Movement-Inducing Fatigue State Based on ECG Signal",
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\textbackslash{}\%\$\$.",
keywords = "ECG, Fatigue analysis, Machine/deep learning",
author = "Jingjing Liu and Jia Zeng and Zhiyong Wang and Honghai Liu",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 15th International Conference on Intelligent Robotics and Applications, ICIRA 2022 ; Conference date: 01-08-2022 Through 03-08-2022",
year = "2022",
doi = "10.1007/978-3-031-13822-5\_61",
language = "英语",
isbn = "9783031138218",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "677--685",
editor = "Honghai Liu and Weihong Ren and Zhouping Yin and Lianqing Liu and Li Jiang and Guoying Gu and Xinyu Wu",
booktitle = "Intelligent Robotics and Applications - 15th International Conference, ICIRA 2022, Proceedings",
address = "德国",
}