Multimodal integration for data-driven classification of mental fatigue during construction equipment operations: Incorporating electroencephalography, electrodermal activity, and video signals

Abstract

Construction equipment operations that require high levels of attention can cause mental fatigue, which can lead to inefficiencies and accidents. Previous studies classified mental fatigue using single-modal data with acceptable accuracy. However, mental fatigue is a multimodal problem, and no single modality is superior. Moreover, none of the previous studies in construction industry have investigated multimodal data fusion for classifying mental fatigue and whether such an approach would improve mental fatigue detection. This study proposes a novel approach using three machine learning models and multimodal data fusion to classify mental fatigue states. Electroencephalography, electrodermal activity, and video signals were acquired during an excavation operation, and the decision tree model using multimodal sensor data fusion outperformed other models with 96.2% accuracy and 96.175%–98.231% F1 scores. Multimodal sensor data fusion can aid in the development of a real-time system to classify mental fatigue and improve safety management at construction sites.

Keywords

• Mental fatigue
• Construction safety
• Construction equipment operators
• Machine learning
• Multimodal data