Study on a Portable Electrode Used to Detect the Fatigue of Tower Crane Drivers in Real Construction Environment

In view of the serious accidents caused by the fatigue operation of tower crane drivers in construction, this study puts forward a novel type of portable semi-dry electrode to detect the mental fatigue state of tower crane drivers in real time and reduce the occurrence of unsafe operation behavior. The electrode not only possesses the advantages of low contact impedance and convenient use as traditional dry and wet electrodes, but also has a more stable performance compared with the semi-dry electrodes designed by previous scholars. It is suitable for experimental occasions such as fatigue detection of tower crane drivers and collecting electroencephalogram (EEG) signals for a long time. In addition, combined with the advantages of the Gramian angular difference field-convolutional neural network (GADF-CNN) algorithm that can extract multiple features of EEG signals for comprehensive fatigue determination, this study chose GADF-CNN algorithm to analyze the driving fatigue features of tower crane drivers. The results show that the combination of the novel semi-dry electrode and GADF-CNN algorithm can detect the driving fatigue features of tower crane drivers in real time and conveniently, thus improving the safety of building construction.