The identification of fatigue in personal workers in particular environments can be achieved through early warning techniques. In order to prevent excessive fatigue of medical workers staying in infected areas in the early phase of the coronavirus disease pandemic, a system of low-load wearable electrocardiogram (ECG) devices was used as intelligent acquisition terminals to perform a continuous measurement ECG collection. While machine learning (ML) algorithms and heart rate variability (HRV) offer the promise of fatigue detection for many, there is a demand for ever-increasing reliability in this area, especially in real-life activities. This study proposes a random forest-based classification ML model to identify the four categories of fatigue levels in frontline medical workers using HRV. Based on the wavelet transform in ECG signal processing, stationary wavelet transform was applied to eliminate the main perturbation of ECG in the motion state. Feature selection was performed using ReliefF weighting analysis in combination with redundancy analysis to optimize modeling accuracy. The experimental results of the overall fatigue identification achieved an accuracy of 97.9% with an AUC value of 0.99. With the four-category identification model, the accuracy is 85.6%. These results proved that fatigue analysis based on low-load wearable ECG monitoring at low exertion can accurately determine the level of fatigue of caregivers and provide further ideas for researchers working on fatigue identification in special environments.
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April 2023
Research Article|
April 04 2023
Research on fatigue identification methods based on low-load wearable ECG monitoring devices Available to Purchase
Huiquan Wang
;
Huiquan Wang
(Methodology, Supervision, Writing – review & editing)
1
School of Life Sciences, TianGong University
, Tianjin 300387, China
2
Tianjin Engineering Research Center of Biomedical Electronic Technology
, Tianjin 300387, China
3
Tianjin Key Laboratory of Quality Control and Evaluation Technology for Medical Devices
, Tianjin 300384, China
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Mengting Han
;
Mengting Han
(Validation, Writing – original draft)
1
School of Life Sciences, TianGong University
, Tianjin 300387, China
2
Tianjin Engineering Research Center of Biomedical Electronic Technology
, Tianjin 300387, China
3
Tianjin Key Laboratory of Quality Control and Evaluation Technology for Medical Devices
, Tianjin 300384, China
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Tasmia Avouka
;
Tasmia Avouka
(Investigation)
1
School of Life Sciences, TianGong University
, Tianjin 300387, China
2
Tianjin Engineering Research Center of Biomedical Electronic Technology
, Tianjin 300387, China
3
Tianjin Key Laboratory of Quality Control and Evaluation Technology for Medical Devices
, Tianjin 300384, China
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Ruijuan Chen
;
Ruijuan Chen
(Methodology, Software)
1
School of Life Sciences, TianGong University
, Tianjin 300387, China
2
Tianjin Engineering Research Center of Biomedical Electronic Technology
, Tianjin 300387, China
3
Tianjin Key Laboratory of Quality Control and Evaluation Technology for Medical Devices
, Tianjin 300384, China
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Jinhai Wang;
Jinhai Wang
(Funding acquisition, Supervision)
1
School of Life Sciences, TianGong University
, Tianjin 300387, China
2
Tianjin Engineering Research Center of Biomedical Electronic Technology
, Tianjin 300387, China
3
Tianjin Key Laboratory of Quality Control and Evaluation Technology for Medical Devices
, Tianjin 300384, China
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Ran Wei
Ran Wei
a)
(Funding acquisition, Supervision)
1
School of Life Sciences, TianGong University
, Tianjin 300387, China
2
Tianjin Engineering Research Center of Biomedical Electronic Technology
, Tianjin 300387, China
3
Tianjin Key Laboratory of Quality Control and Evaluation Technology for Medical Devices
, Tianjin 300384, China
a)Author to whom correspondence should be addressed: [email protected]
Search for other works by this author on:
Huiquan Wang
1,2,3
Mengting Han
1,2,3
Tasmia Avouka
1,2,3
Ruijuan Chen
1,2,3
Jinhai Wang
1,2,3
Ran Wei
1,2,3,a)
1
School of Life Sciences, TianGong University
, Tianjin 300387, China
2
Tianjin Engineering Research Center of Biomedical Electronic Technology
, Tianjin 300387, China
3
Tianjin Key Laboratory of Quality Control and Evaluation Technology for Medical Devices
, Tianjin 300384, China
a)Author to whom correspondence should be addressed: [email protected]
Rev. Sci. Instrum. 94, 045103 (2023)
Article history
Received:
December 08 2022
Accepted:
March 17 2023
Citation
Huiquan Wang, Mengting Han, Tasmia Avouka, Ruijuan Chen, Jinhai Wang, Ran Wei; Research on fatigue identification methods based on low-load wearable ECG monitoring devices. Rev. Sci. Instrum. 1 April 2023; 94 (4): 045103. https://doi.org/10.1063/5.0138073
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