EEG-Based Performance-Driven Adaptive Automated Hazard Alerting System in Security Surveillance Support

Abstract

Automated vision-based hazard detection algorithms are being rapidly developed to provide hazard alerts for construction workers. However, these alerting systems often apply a fixed low-beta alerting threshold, which can cause excessive false alarms, followed by distractions and human distrust in automation. In this study, we propose a novel adaptive automated hazard alerting system capable of adjusting alert threshold levels based on environmental scenarios and workers’ hazard recognition performance evaluated using a wearable electroencephalogram (EEG) sensor system. We designed a hazard recognition experiment consisting of multiple hazardous scenarios and acquired behavioral data and EEG signals from 76 construction workers. We used the linear ballistic accumulator model to decompose hazard recognition into several psychological subcomponents and compared them among different scenarios. Subsequently, our proposed strategy includes clustering of participants’ hazard recognition performance levels based on latent profile analysis, wavelet transform of EEG signals, transfer learning for signal classification, and continual learning to improve the robustness of the model in different scenarios. The results show that the proposed method represents a feasible worker-centered adaptive hazard alerting approach. The anticipated system can be leveraged in a real-world wearable headset application that aims to promote proactive hazard intervention and facilitate human trust in automated hazard alerting technologies.