Jebelli, H; Choi, B and Lee, S H (2019) Application of wearable biosensors to construction sites. I: Assessing workers' stress. Journal of Construction Engineering and Management, 145(12): 04019079, ISSN 0733-9364
Abstract
One of the major hazards of the workplace, and in life in general, is occupational stress, which adversely affects workers' well-being, safety, and productivity. The construction industry is one of the most stressful occupations. Current stress-assessment tools rely either on a subject's perceived stress (e.g., stress questionnaires) or an individual's chemical reaction to stressors (e.g., cortisol hormone). However, these methods can interrupt ongoing tasks and therefore may not be suitable for continuous measurement. To address this problem, the authors aim to develop and validate a framework for noninvasive and nonsubjective measurement of worker stress by examining changes in workers' physiological signals collected from a wearable biosensor. The framework applies various filtering methods to reduce physiological signal noises and extracts the patterns of physiological signals as workers experience various stress levels. Then, the framework learns these patterns by applying a supervised-learning algorithm. To examine the performance of the proposed framework, the authors collected a physiological signal from 10 construction workers in the field. The proposed framework resulted in a stress-prediction accuracy of 84.48% in distinguishing between low and high stress levels and 73.28% in distinguishing among low, medium, and high stress levels. The results confirmed the potential of the proposed framework for assessing workers' stress in the field. Automatic predictions of workers' physical demand levels based on physiological signals is described in a companion paper. This study, along with the companion paper, contributes to the body of knowledge on the in-depth understanding of construction workers' stress on construction sites by developing a noninvasive means for continuous monitoring and assessing workers' stress. The proposed stress-recognition framework is expected to enhance workers' health, safety, and productivity through early detection of occupational stressors on actual sites.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | construction workers' stress prediction; health and productivity; occupational stress; physiological signals; supervised learning; wearable sensor; workers' safety |
| Index terms: | construction site, occupational stres, occupation, learning algorithm, accuracy, stressors, body of knowledge, well-being, questionnaire, productivity, construction worker, construction industry, wearable sensor, monitoring |
| Subjects: | industry analysis, sociology, health conditions and diseases, occupational health and safety management, professional development, computer vision, algorithms, management, knowledge management, work location, control systems, practitioner, mental health and wellbeing, data collection methods |
| Topics: | Digital Applications, Site Management, Business Strategy, Health and Safety, Research Practice, Information Management, Roles and Professions |
| Descriptive scope: | 4 PCTA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here