And there's a lot of physical effort required of factory workers. The strain of repetitive motion can lead to various musculoskeletal injuries, such as carpal tunnel syndrome or tendonitis in the wrists, arms and shoulders. Risks of injury not only cause workers to suffer, but can create massive inefficiencies for companies themselves, through hidden costs such as workers' compensation, lost time and reduced productivity.
"We want to solve these problems before people get hurt," says Rob Radwin, a professor of industrial systems of engineering at the University of Wisconsin-Madison. Radwin has been studying this problem for more than two decades, and with the advent of technology, he may be able to create a solution that is easy, efficient and economically viable.
Existing methods for measuring risk of injury leave much to be desired: Health and safety professionals often make subjective judgments of risk based on a 0-10 scale of hand activity. Although these measurements provide fairly reasonable predictions, there is immense room for error in human observation, and such conclusions require valuable time, expertise and training in ergonomics and safety. It also requires following the nuanced actions of many individuals over a long period of time. Current technology may be the key to facing, and ultimately fixing, this issue.
Radwin and his students are collaborating with Yu Hen Hu, a professor of electrical and computer engineering at UW-Madison. They already have developed computer vision algorithms to calculate hand activity level, funded through exploratory grants from the National Institute for Occupational Safety and Health (NIOSH) and the National Institutes of Health. In September 2016, Radwin received additional three-year funding of $1.4 million from the NIOSH Centers for Disease Control and Prevention.
This new grant will allow Radwin and his colleagues to use videos collected from a variety of institutions—among them, UC-Berkeley, NIOSH, and the State of Washington Department of Labor and Industries—to develop an entirely new measure for assessing health outcomes. This measure will use their video footage to visualize and track repetitive motions—establishing pattern recognition at which the hand demonstrates repetitive movements, grasps and exertions. By combining their recent epidemiology findings with this new measurement, they can create a basis for engineers to measure risk for injuries and redesign certain jobs in the workplace.
"I envision an app, and I think all the technology we need exists on my smartphone today: a high definition camera, a high-speed processor, and the ability to do cloud computing," he says.
If Radwin can apply his measures to a smartphone application, manufacturing employers could assess risk of injury of their employees with relative ease. This would involve simply pointing a handheld video device, which is less intrusive and time-consuming than existing methods, such as attaching an instrument to a worker's arm or hand.
Read Article: phys.org/news/2016-12-smartphone-technology-combat-workplace-injury.html#jCp