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EEG-based workers' stress recognition at construction sites

Title
EEG-based workers' stress recognition at construction sites
Authors
Jebelli, HoutanHwang, SungjooLee, SangHyun
Ewha Authors
황성주
Issue Date
2018
Journal Title
AUTOMATION IN CONSTRUCTION
ISSN
0926-5805JCR Link

1872-7891JCR Link
Citation
AUTOMATION IN CONSTRUCTION vol. 93, pp. 315 - 324
Keywords
Electroencephalogram (EEG)Brain wave patternsConstruction workers&aposstressSupervised learningWorkers&aposhealth, safety, and productivityWearable biosensors in construction sites
Publisher
ELSEVIER SCIENCE BV
Indexed
SCIE; SCOPUS WOS scopus
Document Type
Article
Abstract
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.
DOI
10.1016/j.autcon.2018.05.027
Appears in Collections:
엘텍공과대학 > 건축공학전공 > Journal papers
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