Revealing the “Invisible Gorilla” in construction: Estimating construction safety through mental workload assessment

Abstract Construction companies can accrue losses due to labor fatalities and injuries. Since more than 70% of all accidents are related to human activities, detecting and mitigating human-related risks hold the key to improving the safety conditions within the construction industry. Previous research has revealed that the psychological and emotional conditions of workers can contribute to fatalities and injuries. Recent observations in the area of neural science and psychology suggest that inattentional blindness is one major cause of unexpected human related accidents. Due to the limitation of human mental workload, laborers are vulnerable to unexpected hazards while focusing on complicated construction tasks. Therefore, the ability to detect the mental conditions of workers could reduce unexpected injuries. However, there are currently no available measurement approaches or devices capable of monitoring construction workers' mental conditions. The research proposed in this paper aims to develop a measurement approach to evaluate hazards through neural time–frequency analysis. The experimental results show that neural signals are valid for mental load assessment of construction workers, especially the low frequency bands signals. The research also describes the development of a prototype for a wearable electroencephalography (EEG) safety helmet that enables the collection of the neural information required as input for the measurement approach.