SBIR Phase II: Comprehensive, Human-Centered, Safety System Using Physiological and Behavioral Sensing to Identify Hazards and Predict & Prevent Workplace Accidents

The broader impact of this Small Business Innovation Research (SBIR) Phase II project is to better protect workers from hazards in the workplace through the use of wearable technology to identify, predict and prevent accidents on the job. Workplace safety statistics have not improved in the last several decades. Human-factor related accidents account for 80% or more of injuries and fatalities and are not being adequately addressed with current safety products on the market. The human body provides valuable sensor data in response to hazards. The proposed technology solution will use wearable technology to automate the collection of physiological and behavioral data from workers to be used in Machine Learning Models to identify safety incidents and near-misses. This data will provide the basis for additional Machine Learning Models to predict the likelihood of safety accidents so that safety personnel can intervene before the worker is injured. By better protecting workers, lives will be saved and companies will realize tremendous savings in insurance costs, liabilities and lost time on the job by their employees.

This Small Business Innovation Research (SBIR) Phase II project aims to develop a safety system that uses the human body’s built-in ability to identify and respond to safety hazards. By automating the continuous collection of real-time physiological, emotional and behavioral data using wearable technology, machine learning (ML) models will be developed to identify safety incidents enabling prediction and prevention of workplace accidents. These models have the capability of measuring the intensity of the safety event so that alerts can be issued and lives saved. The proposed research has the following objectives: 1) Collect hazard data and develop new ML models to identify new hazard types known to cause workplace accidents, 2) Research and develop a unique system architecture and the associated wireless hardware for bulk physiological and behavioral data collection across large populations of users in complex industrial environments, 3) Develop ML models to assess risk of future safety incidents. Data will be collected from human subjects that will be subjected to various workplace hazards. The anticipated result of this research is a safety system that can be used by safety personnel to trigger alerts and identify risk levels to help save lives and prevent workplace accidents related key workplace hazards.

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

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.

Award Number: 2449538
Principal Investigator: Daniel Timco
Funds Obligated: $1,204,466
State: WV