I-Corps: Translation Potential of a Safety System Based on Wearable Technology and Artificial Intelligence to Improve Worker Safety

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

This I-Corps project focuses on the development of a wireless wearable technology system that enhances worker safety, ergonomic monitoring, and productivity in high-risk industries such as construction, manufacturing, and healthcare. The system addresses critical gaps in traditional workplace safety methods, which often rely on periodic audits and manual reporting. By providing workers’ real-time positions and motion analysis, the solution enables proactive intervention to reduce workplace accidents and associated costs. This technology has the potential to improve worker well-being, increase operational efficiency, and reduce downtime due to injuries. The project advances national priorities by promoting technological innovation in workforce safety and real-time monitoring, contributing to the progress of science and improvements in health, economic productivity, and workplace well-being. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of an integrated system that combines wearable devices equipped with motion sensors and a cloud-based platform powered by artificial intelligence algorithms. The wearable devices continuously capture and monitor worker movements, enabling the recognition and classification of specific work activities in real time. The cloud platform aggregates and analyzes this data to provide actionable insights into safety, ergonomic assessments, and automated productivity tracking. Scientific advances include the application of machine learning models to accurately recognize worker activities and assess ergonomic risks, representing a significant improvement over traditional manual observation methods. Users benefit from proactive risk mitigation, enhanced worker safety, improved task efficiency, and the scalability of real-time operational monitoring across a variety of high-risk workplaces. 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.