SBIR Phase II: Predicting Musculoskeletal Injury Risk of Material Handling Workers with Novel Wearable Devices

This Small Business Innovation Research (SBIR) Phase II project has the objective of demonstrating that discrete, belt mounted internet-connected wearable devices used by industrial workers can detect high risk lifting activities, promote safe lifting practices and behavior change, and predict the risk of musculoskeletal injuries due to unsafe lifting. Each year over 600,000 workers suffer a musculoskeletal injury due to lifting related activities, which cost US companies over $15bn annually. Worker injuries affect employee morale, absenteeism, productivity loss and employee turnover, all of which are challenges to the efficient running of a company and are a unnecessary cause of human suffering. By developing a wearable device that can detect high risk lifting activity and provide immediate feedback to workers, safer lifting practices can be promoted and a reduction in the number of unsafe lifts registered, leading to a reduction in injuries. The project includes three main technical objectives: i) the development of machine learning algorithms to detect lifting events from sensor data, and to measure risk related metrics associated to those lifting events. When a lift is considered high risk, real-time feedback will be provided to the worker; ii) the deployment of the device in an industrial setting at several customer sites for 12 months, with the number of high risk lifts performed by workers quantified over time to measure the ability of the system to drive behavior change in the workforce; and ii) the development of a model that can predict the likelihood of musculoskeletal injures based on the risk metrics measured. It is expected that the outcomes of the project demonstrate a significant reduction in the risk of suffering musculoskeletal injuries, paving the way for a clear return on investment value proposition for the industrial companies and their insurance carriers who are potential customers.