Given the disproportionate rate of fatalities and injuries in the construction industry and the potential of ambiguous health and hazardous situations with respect to the impending technological revolution and climate change, it is crucial to improve the health and safety of the future workforce. However, there is a lack of an effective, objective, and continuous approach for assessing construction workers' health status at jobsites. Although there have been important innovations in wearable physiological sensing technologies and artificial intelligence for objective assessment of construction workers' health parameters, there remain fundamental challenges for establishing a worker-centered holistic health monitoring approach with promising preventive potentials. These challenges stem from: a) lack of a scalable and feasible wearable sensor for continuous elicitation of workers' diverse bodily responses to stressors in the field; b) lack of a robust interpretive data-driven framework to process the elicited signals for automatic early detection of physical fatigue, mental stress, and exposure to heat stress; and c) lack of effective representation of health and safety information to workers and managers for enabling improved task decisions by augmenting their situational awareness. By establishing a real-time and context-aware holistic health monitoring approach, this project will play a fundamental role in improving the safety of close to 7 million workers in the U.S. construction sector. The developed intelligent health monitoring system is expected to produce changes in the quality of work and workforce policies, resulting in reduced conflicts and enhanced quality of life. It can also be used to address workplace health issues in other hazardous industries such as manufacturing, firefighting, and agriculture.The overarching goal of this research is to improve construction workforce health and safety by integrating multi-disciplinary research in flexible, wearable sensor fabrication, artificial intelligence, and privacy-aware information visualization to provide near-real-time and projected future context-aware health and safety information to workers and managers for enabling improved task decisions by augmenting their situational awareness. The intellectual significance of this project lies in fulfilling the goal by generating and expanding new knowledge on three fronts. First, the project will design and fabricate a flexible wearable sensor for continuous and noninvasive measurement of workers' bioelectric signals and electrochemical responses at construction sites. The use of a single, flexible wearable sensing device instead of multiple off-the-shelf sensors will facilitate the scalability and feasibility of the proposed health sensing system in the construction workplace. Second, the project will develop robust machine learning algorithms and frameworks for continuous and objective assessment of workers' health conditions in the field based on physiological, contextual, and environmental data. For this purpose, this project will address fundamental challenges related to traditional machine learning algorithms by developing a novel interpretive data-driven approach robust to inter- and intra-individual variability while ensuring data security and privacy. Third, this research will generate a digital twin model (health and safety maps) of the construction sites through an array of collective health analyses and develop an automated feedback module for providing personal health-related information and corresponding mitigation strategies to field workers. The insights into the collective health and safety information can profoundly assist the workers and safety managers in making a sound, far-sighted decision about the execution of field-oriented construction operations in near real-time. This research effort will open new doors in improving proactive health and safety management in the field through collective visualization of workers' real-time health and safety information.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
|Effective start/end date
|10/1/22 → 11/30/23
- National Science Foundation: $1,344,500.00
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