Key result
Deep recurrent network using ECG and vital signs detects operator fatigue with ~96% accuracy.
Why the study?
Transmission line maintenance operators face harsh environments, but monitoring equipment and fatigue state assessment methods to track their fatigue status are currently lacking.
Does a fatigue assessment algorithm based on deep recurrent network accurately assess fatigue in operators under simulated operational load?
Does a fatigue assessment algorithm based on deep recurrent network accurately assess fatigue in operators under simulated operational load?
A deep recurrent network-based algorithm using ECG, body temperature, and blood oxygen data can accurately assess fatigue in operators.
Suggests high-accuracy fatigue detection in simulation; leaves open real-world validation before operational use.
The transmission line maintenance operation environment is harsh, and if the operators suffer from relevant diseases or poor physical and mental status, they are highly vulnerable to danger. Currently, there is a lack of monitoring equipment and fatigue state assessment methods, and the supervisors cannot grasp the fatigue status of the operators. Therefore, this paper designs a multi-source physiological signal measurement circuit, collects physiological signal data of 10 operators under simulated operational load, extracts the nonlinear characteristics of ECG signals, and constructs a fatigue assessment sample library by combining body temperature and blood oxygen data. Subsequently, this paper designed a fatigue assessment algorithm based on deep recurrent network, trained and tested the algorithm using the fatigue assessment sample library, and achieved an assessment accuracy of 95.6%.
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Li et al. (2022) studied Fatigue (n=10). Fatigue assessment algorithm based on deep recurrent network was evaluated on Assessment accuracy. A fatigue assessment algorithm based on a deep recurrent network using ECG, body temperature, and blood oxygen data achieved an assessment accuracy of 95.6% in operators under simulated load.
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