Sbir phase i: comprehensive, human-centered, safety system using physiological and behavioral sensing to predict and prevent workplace accidents

The National Science Foundation (NSF) awarded a $273,369 Project Grant to Intellisafe Analytics LLC for a Small Business Innovation Research (SBIR) Phase I project titled "Comprehensive, Human-Centered, Safety System Using Physiological and Behavioral Sensing to Predict and Prevent Workplace Accidents". The goal of this project is to develop a safety system that uses wearable technology to continuously collect real-time physiological and behavioral data from workers, and applies machine learning models to identify safety incidents and predict the likelihood of accidents. This innovative approach aims to enable the prediction and prevention of workplace accidents, which account for a significant portion of workplace injuries. The intellectual merit of the research includes verifying human responses to slips and trips, developing machine learning models to identify these safety incidents, and assessing future accident risk to provide real-time alerts. This SBIR Phase I project is funded through the NSF Technology, Innovation, and Partnerships (CFDA 47.084) program, which supports convergent research and development to translate scientific discoveries into commercially viable technologies and solutions.

Abstract

The broader/commercial impact of this small business innovation research (sbir) phase i project is to better protect workers from hazards in the workplace through the use of wearable technology to identify and predict accidents. Human-factor related accidents account for 80% of injuries and are not being addressed with currently available safety products. This solution utilizes wearable technology to automate the collection of physiological and behavioral data from workers. The data is incorporated into machine learning models to identify safety incidents and near-misses. This innovative approach to worker safety enhances scientific and technological understanding by using machine learning to interpret signals generated by a worker?s physiology and behaviors. Responses to hazards in the workplace are used to trigger alerts that predict and prevent workplace accidents. This safety system provides the basis for machine learning models that predict the likelihood of accidents so safety personnel can intervene before the worker is injured. The goal of this project is to prevent injuries, save lives, and enable companies to realize savings in insurance costs, liabilities, and lost time from the job. This sbir phase i project aims to develop a safety system that uses the human body?s built-in sensors to identify safety hazards. By automating the continuous collection of real-time physiological and behavioral data using wearable technology, machine learning models will be developed to identify safety incidents, enabling the prediction and prevention of accidents. The intellectual merit of the research is to: 1) verify that humans respond in similar, measurable ways to slips and trips, 2) develop machine learning models to accurately identify slips and trips and their intensity, 3) develop machine learning models to assess the risk of future safety accidents, and 4) verify that data can be processed through the entire workflow to provide real-time alerts to the worker and safety personnel. Data will be collected from human subjects subjected to slips and trips using research-grade wearables. The anticipated output of this research will provide the basis for a safety system used to trigger safety alerts and identify risk levels to save lives and prevent accidents related to slips and trips. 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.- subawards are planned for this award.