Neurologists just came up with a new approach that could make rehabilitation from a stroke simpler and more efficient
Neurologist Heidi Schambra from New York University describes the post-stroke brain as “avid to learn”: in the initial weeks of recovery, the brain is ripe to reform its connection to muscle and regain abilities that once came naturally. “There’s a window of opportunity where we can really engage the brain,” Schambra said. “There's this heightened plasticity that the stroke itself sets into action.
Without clarity on how much training a patient is getting, it’s hard to figure out how to tweak it. In a new study, Schambra and other colleagues at NYU outline a new approach they’ve developed to pin down that data, using AI to count and identify arm motions about 370 times faster than human observers can. It may be able to help stroke patients get closer to the right quantities of motions needed to jumpstart their recovery.
The team then trained the AI algorithms on these thousands of examples of motion and on the patterns of data representing each class—like a “reach” or a “transport”—until the program was able to count and classify the motions on its own. The resulting tool, which can process 6.4 hours of recorded activities in 1.4 hours versus the 513.6 hours human coders needed, brings a combination of pragmatism and precision the field has never seen before, Schambra said.
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