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