Houtan Jebelli, Assistant Professor of Architectural Engineering and Director of the Robotic, Automation and Intelligent Sensing (RAISe) Lab is leading a research team from Penn State University and Ohio State University to develop an artificial intelligence (AI)-enabled, real-time and context-aware holistic health monitoring approach for construction workers following a four-year, $1.8 million National Science Foundation (NSF) grant.
With more than 1,000 fatal work injuries in 2020 and 169,200 nonfatal work injuries in 2021, construction workers face significant health and safety hazards on the job, so a worker-centered holistic health monitoring approach could be a first step toward improving workplace health and safety on construction sites and eventually in a broad range of industries, says Jebelli.
He explains that existing health assessment tools have several drawbacks, as assessments rely on surveys, which can be subjective and disrupt work. While wearable sensors, such as headsets that measure brain activity or wristbands that measure cardiac activity, cannot record the multiple physiological signals together.
For this reason his team is using the grant to design and fabricate a flexible wireless sensing device that can capture workers' diverse physiological signals and biological markers to the stressors in the field and can be worn in a way that is non-invasive and non-disruptive to workers. “We also want to develop innovative machine learning algorithms and frameworks to infer meaningful cues from the elicited bodily responses for continuous and real-time assessment of workers' holistic health conditions. Importantly, we must also maintain workers' privacy,” Jebelli says.
Jebelli says that the research has the potential to be applied broadly in an effort to benefit works safety and security. “Our envisioned worker-centered health-monitoring mechanism based on advances in wearable sensor fabrication, predictive analytics and privacy-aware information visualization could be applied beyond the construction industry,” he says. “It could be used for the military, mining, manufacturing, professional athletes’ health monitoring and many other fields."
"The researchers plan to develop a privacy-aware digital twin-assisted mechanism, which could both provide individual workers with personalized feedback on their health — for example, letting them know if they are becoming overheated so they can take steps to correct that — while also de-identifying the health measures from the individuals to create digital health maps that represent collective health information at a job site without compromising workers' privacy.
“The goal is to integrate the crowd sensing of workers' de-identified health measures and digital twin technology to generate a comprehensive health map of the construction workplace so that the safety managers get the aggregated health information of the job site,” Jebelli says. “To ensure workers' privacy, managers won't be able to identify the specific worker who is exposed to, for example, high physical fatigue. But they could identify the locations at the job site where stress is highest or where workers are most fatigued and then better identify the hazards of their job site.”
The co-principal investigators on the grant are John Messner, Charles and Elinor Matts Professor of Architectural Engineering; Huanyu “Larry” Cheng, James L. Henderson, Jr. Memorial Associate Professor of Engineering Science and Mechanics; Mehdi Kiani, Associate Professor of Electrical Engineering; and Jennifer Graham-Engeland, Associate Professor of Biobehavioral Health, all at Penn State.
For more information on the project, click here.