Study on tower crane drivers' fatigue detection based on conditional empirical mode decomposition and multi-scale attention convolutional neural network

Tower crane drivers' fatigue may cause safety hazards and serious work accidents. Therefore, the detection of fatigue is critically important. In the research field of driving fatigue of tower crane drivers, the detection method of electroencephalogram (EEG) signals based on drivers is one of the most commonly used methods. However, noise in the real building environment often disrupts this type of detection method, leading to low classification accuracy. To solve this problem, this study proposes a driving fatigue detection model based on conditional empirical mode decomposition and multi-scale attention convolutional neural network (CEMD-MACNN). Conditional empirical mode decomposition (CEMD) overcomes the problem that traditional empirical mode decomposition (EMD) ignores important information or does not sufficiently remove the noise component when analyzing the signal. A multi-scale attention convolutional neural network (MACNN) uses channel attention to adaptively select channels containing fatigue features when extracting features at different scales, thus improving the model's noise immunity and suppressing the influence of noise. In this study, the driving fatigue detection experiment of tower crane drivers was carried out. The Emotiv device was used to collect the EEG signal of 10 subjects in 7 driving stages, and the EEG signals were divided into awake state and fatigue state using the Karolinska sleepiness scale (KSS). The results showed that the CEMD-MACNN methods achieved an average classification accuracy of 98.70% across 10 subjects. Compared with other traditional methods, CEMD-MACNN has better anti-noise performance and higher classification accuracy.