The construction industry continues to suffer from a very high rate of injuries. In 2020 there were more than 1,000 fatal work injuries and in 2021 there were 169,200 nonfatal work injuries.
To address this situation, the National Science Foundation is providing a four-year, $1,8000,000 grant to Penn State and The Ohio State University to develop an artificial intelligence (AI)-enabled, real-time, and context-aware holistic health monitoring approach for construction workers.
Houtan Jebelli, an assistant professor is leading a research team. In an article on Penn State's website, he notes that while currently there are wearable sensors, such as headsets that measure brain activity or wristbands that measure cardiac activity, there are not any sensors available that can record multiple physiological signals together.
“We want 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,” he said, in a statement. “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.”
In terms of technical development, the researchers will use a digital twin-assisted mechanism, which can be used for simulations as well as forecasting. This system is able to preserve employee privacy as it creates a digital health map with information that isn't traced to specific employees. But the system is able to provide personalized health to specific employees. One example cited is letting them know if they are becoming overheated so they can take steps to correct that.
“The goal is to integrate the crowdsensing 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 said. “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.”
Another advantage this project will address is the ability to filter our irrelevant data by using computationally efficient algorithms for real-time noise removal of signals in the field. “When we do physiological sensing for a subject at a hospital, for example, we can catch a high-quality signal in real-time because we can control the environment and subjects movement,” Jebelli said. “But when you are doing physiological sensing in the field, we can not control the environment, and we cannot ask workers to stay in a stationary condition because movement is the nature of the job. High-intensity activity brings a lot of signal noise, and current signal processing methods are not robust in reducing such noise in real-time. We want to address this.”
Photo Caption
From left to right: graduate student Amit Ojha, The James L. Henderson, Jr. Memorial Associate Professor of Engineering Science and Mechanics Huanyu "Larry" Cheng and Assistant Professor of Architectural Engineering Houtan Jebelli examine the performance of the proposed flexible sensing mechanism.