A new system uses machine learning to monitor factory and warehouse workers and tell them how risky their behaviors are in real time, researchers report.
In 2017 there were nearly 350,000 incidents of workers taking sick leave due to injuries affecting muscles, nerves, ligaments, or tendons—like carpal tunnel syndrome—according to the U.S. Bureau of Labor Statistics. Among the workers with the highest number of incidents were people who work in factories and warehouses.
Musculoskeletal disorders happen at work when people use awkward postures or perform repeated tasks. These behaviors generate strain on the body over time. So it’s important to point out and minimize risky behaviors to keep workers healthy on the job.
The new algorithm divides up a series of activities—such as lifting a box off a high shelf, carrying it to a table, and setting it down—into individual actions and then calculates a risk score associated with each action.
Automated Assessment for Warehouse Workers
“Right now workers can do a self-assessment where they fill out their daily tasks on a table to estimate how risky their activities are,” says senior author Ashis Banerjee, an assistant professor in both the industrial & systems engineering and mechanical engineering departments at the University of Washington.
“But that’s time consuming, and it’s hard for people to see how it’s directly benefiting them. Now we have made this whole process fully automated. Our plan is to put it in a smartphone app so that workers can even monitor themselves and get immediate feedback.”
For these self-assessments, people currently use a snapshot of a task being performed. The position of each joint gets a score, and the sum of all the scores determines how risky that pose is. ..
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