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| Funder | National Science Foundation (US) |
|---|---|
| Recipient Organization | Louisiana State University |
| Country | United States |
| Start Date | Apr 01, 2025 |
| End Date | Mar 31, 2026 |
| Duration | 364 days |
| Number of Grantees | 1 |
| Roles | Principal Investigator |
| Data Source | National Science Foundation (US) |
| Grant ID | 2453821 |
The I-Corps project is focused on the commercialization of a software system for early identification of high-risk situations in the workplace, seeking to prevent accidents for industrial workers. This technology could help companies in the construction, environmental engineering, and industrial manufacturing industries where there are significant risks of worker injuries and worker compensation costs are high.
With this technology, both management and workers benefit from real-time alerts, risk assessment, and accessible analytics. Improved systems to protect workers from safety hazards could result in fewer workplace injuries, illnesses, and fatalities, which have serious consequences for individuals, businesses, and society.
This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a software platform designed for industrial safety. The technology utilizes a scalable cloud architecture and web application coupled with a mobile application, wearable and stationary Internet of Things (IoT) sensors, and cognitive assessments to monitor and alert worker risk in real time.
By incorporating a high throughput data pipeline for processing biometric, environmental, and cognitive data, reportable safety incident identification and mitigation can occur when risk factors are high, potentially before a safety incident arises. Included in the system’s capabilities are configurable alerts for different safety conditions, historical analyses to identify safety trends, and the ability to incorporate different sensors and devices based upon users’ needs and preferences.
Artificial intelligence capabilities trained on the repository of collected safety data is used to infer the presence of high-risk situations and predict critical intervention steps before situations escalate, preventing worker injury.
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.
Louisiana State University
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