Wearable stroke rehabilitation device gives patients the upper hand

A team led by University of Massachusetts Amherst researchers has developed a wearable wrist device powered by a machine-learning algorithm that can continuously track changes in arm movement impairment after a stroke. Monitoring those changes throughout rehabilitation could allow clinicians to adjust therapy in real time, tailoring care instead of relying on the current one-size-fits-all approach.

“We are the first group to actually show that, using wearable data, we can extract information about patients’ motor severity, which clinicians can actually use to determine whether their intervention is effective or not,” says Sunghoon Ivan Lee, an associate professor in the Manning College of Information and Computer Sciences at UMass Amherst and corresponding author of the paper describing the technology.

The research was conducted with colleagues from Washington University in St. Louis, Shirley Ryan AbilityLab and Harvard Medical School/Mass General Brigham.

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