Sports fatigue detection based on deep learning

Xiaole Guan; Yanfei Lin; Qun Wang; Zhiwen Liu; Chengyi Liu

doi:10.1109/CISP-BMEI53629.2021.9624395

Sports fatigue detection based on deep learning

Xiaole Guan
, Yanfei Lin
, Qun Wang
, Zhiwen Liu
, Chengyi Liu

School of Integrated Circuits and Electronics

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

23 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021
Editors	Qingli Li, Lipo Wang, Yan Wang, Wenwu Li
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665400039
DOIs	https://doi.org/10.1109/CISP-BMEI53629.2021.9624395
Publication status	Published - 2021
Event	14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021 - Shanghai, China Duration: 23 Oct 2021 → 25 Oct 2021

Publication series

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

Conference

Conference	14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021
Country/Territory	China
City	Shanghai
Period	23/10/21 → 25/10/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Bi-LSTM
Deep learning
ECG
HRV
Sports fatigue recognition
acceleration velocity
angular velocity

Access to Document

10.1109/CISP-BMEI53629.2021.9624395

Cite this

Guan, X., Lin, Y., Wang, Q., Liu, Z., & Liu, C. (2021). Sports fatigue detection based on deep learning. In Q. Li, L. Wang, Y. Wang, & W. Li (Eds.), Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021 (Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CISP-BMEI53629.2021.9624395

Guan, Xiaole ; Lin, Yanfei ; Wang, Qun et al. / Sports fatigue detection based on deep learning. Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021. editor / Qingli Li ; Lipo Wang ; Yan Wang ; Wenwu Li. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021).

@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",

}

Guan, X, Lin, Y, Wang, Q, Liu, Z & Liu, C 2021, Sports fatigue detection based on deep learning. in Q Li, L Wang, Y Wang & W Li (eds), Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021. Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021, Institute of Electrical and Electronics Engineers Inc., 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021, Shanghai, China, 23/10/21. https://doi.org/10.1109/CISP-BMEI53629.2021.9624395

Sports fatigue detection based on deep learning. / Guan, Xiaole; Lin, Yanfei; Wang, Qun et al.
Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021. ed. / Qingli Li; Lipo Wang; Yan Wang; Wenwu Li. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Sports fatigue detection based on deep learning

AU - Guan, Xiaole

AU - Lin, Yanfei

AU - Wang, Qun

AU - Liu, Zhiwen

AU - Liu, Chengyi

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Bi-LSTM

KW - Deep learning

KW - ECG

KW - HRV

KW - Sports fatigue recognition

KW - acceleration velocity

KW - angular velocity

UR - https://www.scopus.com/pages/publications/85123502515

U2 - 10.1109/CISP-BMEI53629.2021.9624395

DO - 10.1109/CISP-BMEI53629.2021.9624395

M3 - Conference contribution

AN - SCOPUS:85123502515

T3 - Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021

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

A2 - Li, Qingli

A2 - Wang, Lipo

A2 - Wang, Yan

A2 - Li, Wenwu

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021

Y2 - 23 October 2021 through 25 October 2021

ER -

Guan X, Lin Y, Wang Q, Liu Z, Liu C. Sports fatigue detection based on deep learning. In Li Q, Wang L, Wang Y, Li W, editors, Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021. Institute of Electrical and Electronics Engineers Inc. 2021. (Proceedings - 2021 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021). doi: 10.1109/CISP-BMEI53629.2021.9624395

Sports fatigue detection based on deep learning

Abstract

Publication series

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UN SDGs

Keywords

Access to Document

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