Video tool could help active workers avoid injury

Posted on 01. May, 2013 by in Academic Departments, Biomedical Engineering, Electrical and Computer Engineering, Healthcare and Medicine, Industrial and Systems Engineering, Issues, Magazine, Mechanical Engineering, Research

Assembly workersVideo of workers performing tasks such as assembling a manufactured part or packing boxes could help assess the likelihood that those people will develop common repetitive-motion injuries.

Repeatedly performing the same motions and exertions can lead to injuries in which the body suffers strain from even tiny actions—for example, handling small parts, operating a machine or using a tool hundreds or thousands of times a day. “Companies want to understand how much time people should spend doing certain kinds of jobs,” says Robert Radwin, a professor of industrial and systems engineering and biomedical engineering. “There’s currently no convenient way to do that. Sometimes the way injuries come to the attention of the company is that someone gets injured, and then it’s too late.”

The assessment tool, which automatically measures and analyzes repetitive tasks based solely on video recordings, could help companies predict and alleviate the risk of injuries such as carpal tunnel syndrome or tendonitis.

Radwin and his collaborators published details of the system online recently in the journal Human Factors.

Their new method simply relies on a computer to analyze videos of the workers’ hand movements. “We can do these analyses for a very low cost, which should be really valuable for companies,” Radwin says.

In their study, Radwin and his colleagues found that automated video analysis closely tracked manual observations of workers’ motion cycles, particularly at higher levels of activity. The group, which in November received $800,000 from the National Institute for Occupational Safety and Health and the National Institutes of Health, now is refining and testing the tool—initially, on carpal tunnel syndrome.

In the future, the tool could incorporate algorithms that enable companies to analyze video in real time. “One of our visions is to take an iPhone or camera-enabled hand-held device, hold it up, punch a few things on the screen, and then it works like a sound level meter to tell you the risk of the activity,” says Radwin, who is commercializing the tool through his spin-off company, KineVid.

His collaborators include Yu Hen Hu, a professor of electrical and computer engineering and computer sciences; Mary Lindstrom, a professor of biostatistics; Darryl Thelen, an associate professor of mechanical engineering, Thomas Yen, a biomedical engineering instructor, and Eric Chen, an electrical and computer engineering graduate student.

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