Emotionally intelligent construction safety monitoring via integration of machine learning and expert-informed electroencephalogram-related features

This article proposes an emotionally intelligent construction safety monitoring model that integrates machine learning and expert-informed Electroencephalogram (EEG)-related features as brain wave measurement device. The proposed safety prediction model is trained with risky behaviours, related emotional states, and expert-informed EEG-related features, incorporating brain waves and corresponding cortex locations. The architecture of the proposed emotionally intelligent safety monitoring system is detailed, utilizing real-world construction datasets and questionnaires. The value of the proposed emotionally informed ML framework lies in introducing EEG-related emotional constructs into construction safety assessment through an exploratory, expert-informed representation of emotional intelligence. The addition of a new dimension to the construction safety prediction models and consideration of the versatility of factoring in emotional states makes implications of this study beyond safety to other fields of construction management areas.