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There are some limitations in the use of traditional electroencephalogram (EEG) signal acquisition sensors. For example, the conductive paste of wet electrodes volatilizes faster and the operation process is cumbersome. However, the dry electrode has a large contact impedance value when it is used, which affects the quality of the collected signal. The traditional semi-dry electrode may cause a small movement of the electrode when the supplementary conductive solution is manually pressed, thus affecting the accuracy and stability of the signal. To address these issues, this study proposes a novel electrode that can automatically replenish the conductive liquid. The electrode can monitor the humidity of the electrode in real time through the humidity-sensitive material inside the electrode and realize the automatic supplementation of conductive liquid to the electrode. This kind of electrode improves the efficiency of the conductive liquid, increases the stability of the acquisition signal, and realizes the function of long-term uninterrupted acquisition of EEG signals. In addition, to solve the problem of time-consuming and labor-consuming lifting alignment caused by the uncoordinated communication between the tower crane driver and the lower preform lifting operator, this study proposes a method to improve the efficiency of preform lifting alignment based on the deep residual shrinkage network (DRSN). This method can effectively classify the motor imagery (MI) EEG signals of the preform hoisting operator, then convert the classification results into the instructions of the tower crane, and finally realize the accurate movement and alignment of the preform. The novel electrode can effectively collect EEG signals in the real building environment and prolong the effective use time compared with the traditional semi-dry electrode. The DRSN-based method shortens the time of preformed lifting and alignment and improves efficiency. Compared with other traditional methods, the DRSN algorithm performs better in terms of classification accuracy and anti-noise performance.

Dry electrodes used for electroencephalography (EEG) signals acquisition are of good portability. However, the large impedance of the dry electrode often results in poor signal acquisition quality. Meanwhile, the preparation of wet electrodes in experiments is complex, and replenishing the conductive gel is not convenient. Therefore, based on the comprehensive consideration of the trade-off between wet and dry electrodes used for data acquisition, a novel valve-type semidry electrode is proposed. The semidry electrode has the advantages of convenient wearing and convenient replenishment of conductive liquid. In addition, this experiment is conducted in a real building construction environment, and the acquired EEG signals are highly susceptible to being mixed with various noises. In this study, a deep residual shrinkage networks (DRSNs) algorithm with strong antinoise ability is used to construct a recognition model of construction equipment operators’ mental fatigue using EEG-based features. The DRSN algorithm removes mixed noise and redundant features to improve the recognition accuracy of mental fatigue. These results show that the novel valve-type semidry electrode can collect EEG signals with fatigue characteristics just as well as the wet electrode. The ability to maintain small impedances for a long time can benefit long-period EEG signal acquisition. Additionally, the DRSN algorithm was used to classify the mental fatigue state of 12 subjects. The average classification accuracy is 99.59%; the average precision is 96.40%; the average recall is 97.45%; and the average F1 score is 96.93%. The DRSN algorithm outperforms existing approaches in terms of classification accuracy and noise resistance. This study developed the novel valve-type semidry electrode and the DRSN algorithm to successfully measure mental fatigue changes in construction equipment operators over an extended period in a real building construction setting.

In view of the complicated placement process of wet electrodes used for driving fatigue detection based on electrocardiogram (ECG) characteristics, this study proposed a novel elastic dry electrode for real-time acquisition of ECG signals from the palm of hand. This type of dry electrode eliminated the need for applying conductive paste when collecting the driver’s ECG. Meanwhile, it avoids the requirement of penetrating the cuticle layer of the skin surface to reduce impedance as well, thus minimizing the risk of skin infection. To alleviate driver fatigue resulting from prolonged driving, a periodic man–machine interaction mode (PMIM) was proposed. To assess the efficacy of PMIM in mitigating driver fatigue, this study compared and evaluated driver mental fatigue based on electroencephalogram (EEG) and heart rate variability (HRV) characteristics across two driving modes. The findings demonstrated that the novel elastic dry electrode effectively captured changes in driving fatigue characteristics by acquiring ECG signals from the palm of the hand. In addition, the proposed PMIM effectively mitigated driving fatigue resulting from prolonged periods of driving.

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 the 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.

To address issues with the conventional wet electrode during long-period experiments, which include signal acquisition distortion caused by conductive liquid volatilization and drying, as well as time-consuming conductive liquid refilling, this study introduced a portable semi-dry electrode that allows for convenient conductive liquid replenishment at any time. The new semi-dry electrode combines the advantages of the small impedance value of wet electrodes and the convenient installation and disassembly of dry electrodes. And this electrode is suitable for collecting human electroencephalogram (EEG) for a long time, such as detecting the EEG characteristics of drivers who drive vehicles for a long time. In addition, since the order recurrence plot (ORP) does not require signal stability, data length, and signal-to-noise ratio, this article used the method to analyze the driving fatigue characteristics of the subjects. First, the ORP method was used to map EEG signals collected by the two types of electrodes, and the ORP was performed by recurrence quantitative analysis (RQA). Then, two quantitative parameters, determinism (DET) and average diagonal line length (DLL), were selected to analyze the fatigue-driving characteristics of the subjects. The results showed that with the increase of fatigue degree of the subjects, the blue and white squares in ORP from EEG signals collected by the two kinds of electrodes gradually increased, with a clearer texture and a more organized arrangement. Meanwhile, the quantization parameters DET and DLL increase gradually as well, which can effectively express the law of driving fatigue change. The results of this study are helpful to address the problems such as the difficulty of supplementing conductive fluid with traditional wet electrodes and to address the problem of easy interference in EEG acquisition experiments in complex environments.