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NEWS
Researchers Develop App To Monitor Warehouse Ergonomics
September 11, 2019

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: 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.

According to information, researchers at the University of Washington (UW) have used machine learning to develop a new system that can monitor factory and warehouse workers and tell them how risky their behaviors are in real time. The 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.

The team published its results recently in IEEE Robotics and Automation Letters and presented the findings at the IEEE International Conference on Automation Science and Engineering in Vancouver, British Columbia.

“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,” said senior author Ashis Banerjee, an assistant professor in both the industrial & systems engineering and mechanical engineering departments at the UW. “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. But workers usually perform a series of motions for a specific task, and the researchers wanted their algorithm to be able to compute an overall score for the entire action.

Moving to video is more accurate, but it requires a new way to add up the scores. To train and test the algorithm, the team created a dataset containing 20 three-minute videos of people doing 17 activities that are common in warehouses or factories.

The researchers captured their dataset using a Microsoft Kinect camera, which recorded 3D videos that allowed them to map out what was happening to the participants’ joints during each task.

Using the Kinect data, the algorithm first learned to compute risk scores for each video frame. Then it progressed to identifying when a task started and ended so that it could calculate a risk score for an entire action.

The algorithm labeled three actions in the dataset as risky behaviors: picking up a box from a high shelf, and placing either a box or a rod onto a high shelf.

Now the team is developing an app that factory workers and supervisors can use to monitor in real time the risks of their daily actions. The app will provide warnings for moderately risky actions and alerts for high-risk actions.

Funding and support for this project has been provided by the State of Washington, Department of Labor and Industries, Safety and Health Investment Projects. This research was also funded by a gift from Amazon Robotics.


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