ABSTRACT

Repetitive motion injuries in the workplace impose a significant socioeconomic burden and substantial personal toll on health, prosperity and wellbeing. Manual lifting is an important occupational health and safety concern and is highly prevalent in warehousing, distribution centers, package delivery, transportation, and lean manufacturing. For prevention of work-related injuries, it has therefore become increasingly important to frequently or continuously monitor workers’ exposure to physical demands for varying job tasks. Practical methods for evaluating these types of jobs are currently unavailable, and safety practitioners may not have the resources to observe lifting continuously in numerous locations. Consequently, many hazardous jobs remain unresolved or unrecognized until an injury occurs, resulting in unrecoverable losses to both employers and employees. We are using computer vision and machine learning algorithms for automatically, objectively, and unobtrusively measuring repetitive motion and manual lifting activities. These methods have the potential to be implemented on hand-held devices, such as smartphones, making it widely accessible.

BIOGRAPHY

Professor Radwin is the Duane H. and Dorothy M. Blumke Professor in the College of Engineering at the University of Wisconsin-Madison. He is widely known for his work on biomechanics and hand tools and has introduced new approaches for studying muscle strain, grip and hand exertions, and ways to measure and quantify physical stress in the workplace. He has received numerous awards as an innovator and researcher, is a fellow of five professional societies, and is the Editor in Chief for the journal Human Factors. He is founding chair of the UW-Madison Department of Biomedical Engineering and is a Discovery Fellow of the Wisconsin Institute for Discovery.