Smartphone recording video of a conveyor belt  Credit: Stephanie Precourt/University of Wisconsin-MadisonSmartphone recording video of a conveyor belt Credit: Stephanie Precourt/University of Wisconsin-MadisonA smartphone application could play a key role in assessing factory employees’ injury risk. Research at the University of Wisconsin-Madison is taking the first steps towards building a system that could track patterns of hand movement that can lead to repetitive-motion damage.

Rob Radwin, professor of industrial systems engineering, and some of his students are partnering with electrical and computer engineering professor Yu Wen Hu to develop an improved system for observing factory tasks and pinpointing sources of repetitive motion injury.

“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,” Radwin says.

Current methods for measuring injury risk require health and safety professionals to make subjective judgments based on a scale of hand activity. Although these measurements can yield reasonable predictions, human observations are error-prone, and the process is time-consuming.

Thus far, students have developed computer vision algorithms to calculate workers’ hand activity levels. They plan to use video footage captured by smartphones to track repetitive motion, training computers to recognize patterns of hand movement required to perform repetitive movements, grasping and exerting force.

By combining their hand activity measurements with the computerized ability to spot movement patterns, they can create a new, more objective basis for measuring injury risk — knowledge that could help redesign jobs and make the workplace safer.

Programming smartphones to measure and quantify motions could bring the power of ergonomics to medium and small companies that otherwise could not afford this technology.

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