Taking into account that many construction workers suffer from excessive stress that adversely impacts their safety and health, early recognition of stress is an essential step toward stress management. In this regard, an electroencephalogram (EEG) has been widely applied to assess individuals’ stress by analyzing brain waves in the clinical domains. With recent advancements in wearable EEG devices, EEG's ability can be extended to field workers, particularly by non-invasively assessing construction workers’ stress. This study proposes a procedure to automatically recognize workers’ stress in construction sites using EEG signals. Specifically, the authors collected construction field workers’ EEG signals and preprocessed them to capture high-quality signals. Workers’ salivary cortisol, a stress hormone, was also collected to label low or high-stress levels when they work at sites. Time and frequency domain features from EEG signals were calculated using fixed and sliding windowing approaches. Finally, the authors applied several supervised learning algorithms to recognize workers’ stress while they are working at sites. The results showed that the fixed windowing approach and the Gaussian Support Vector Machine (SVM) yielded the highest classification accuracy of 80.32%, which is very promising given the similar accuracy of stress recognition in clinical domains where extricate and wired EEG devices were used and the subjects engage in minimal body movement. The results demonstrate that the proposed field stress recognition procedure can be used for the early detection of workers’ stress, which can contribute to improving workers’ safety, health, wellbeing, and productivity.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Civil and Structural Engineering
- Building and Construction