I-Corps: A machine learning approach to reduce musculoskeletal disorders-related injuries among workers performing labor-intensive repetitive tasks

Award ID: 2147869

The broader impact/commercial potential of this I-Corps project is the development of a technology that may provide a task-specific optimal ergonomic solution to reduce the risk of musculoskeletal disorders-related injuries (MSDs) among workers performing labor-intensive repetitive tasks. The total cost of MSDs in the US is between $45-54 billion a year, which is around $15,000 per injury. The technology has a wide range of applications in industries like construction, moving services, grocery stores, assemble lines, cleaning services, and warehouses where labor-intensive repetitive tasks are prevalent. Workers exposed to MSD-related injuries suffer a loss in quality of life, missed wages, and increased health expenses. This proposed technology may help them live a healthier and better life. Managers supervising workers may be able to maintain the work schedule and quality of work because of reductions in health issues on site and reduced need to train new workers to replace the injured ones. The companies deploying the workers potentially may observe increased productivity and decreased workers compensation-related claims. The proposed technology also may be useful to the general public by improving the ergonomics of their day-to-day manual repetitive tasks. This I-Corps project is based on the development of a software technology utilizing a machine-learning algorithm to predict unique actions and moments induced in different body joints to compute a safe working procedure for labor-intensive repetitive activities. Human skeletal tracking technology, such as a depth-sensing camera, is used to collect postural data of workers performing their tasks. This real-time posture data serves as the input for the machine learning model. The common practice in industry is to comply with the minimum safety standards prescribed by a regulatory body. Contrary to the practice, the project implements a novel method to identify the maximum achievable level of safety on site, the Safety Frontier, which may serve as a higher benchmark in monitoring safety in labor-intensive repetitive tasks. In addition, the project identifies Sustainable Safety, an optimal ergonomic solution that may be achieved and sustained under normal working conditions. The introduction of these safety levels may advance the understanding of safety dynamics and provide a holistic approach to analyze safety of workers. 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.

NSF Program Director: Ruth Shuman