Computer tool to track stroke rehabilitation to boost recovery

Published On 2022-06-23 03:30 GMT   |   Update On 2022-06-27 07:48 GMT

According to a new study, a sensor-equipped computer program can accurately identify and count arm movements in people undergoing stroke rehabilitation, Now that it can do so, the next step is to use the tool to define the intensity of movements that bring about the greatest recovery in patients' ability to move independently and take care of themselves after a stroke. The study showed that...

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According to a new study, a sensor-equipped computer program can accurately identify and count arm movements in people undergoing stroke rehabilitation, Now that it can do so, the next step is to use the tool to define the intensity of movements that bring about the greatest recovery in patients' ability to move independently and take care of themselves after a stroke.

The study showed that the tool was 77% effective in identifying and counting the number of arm motions prescribed during rehabilitation exercises for stroke patients. Sensors strapped to the arms and back were used to track movements in three dimensions. The developers say they plan further testing on more stroke patients to refine their computer model, cut down on the number of sensors needed, and then develop a smaller prototype device that could be worn on the arm and upper body.

Published in the journal PLOS Digital Health, this study recorded the upper body movements of 41 adult stroke patients while they performed routine rehabilitation exercises for regaining use in the arms and hands. Exercises and arm movements involved patients feeding themselves with a fork and grooming themselves with a comb.

More than 51,616 upper body movements were recorded from nine sensors, with the digital recordings of each arm movement then matched to functional categories, such as whether the movement involved reaching for an object or holding it still.

Artificial intelligence (machine learning) software was then programmed to detect patterns within the data and tie these patterns to specific movements. The resulting PrimSeq tool was then tested on a separate group of eight stroke patients who wore the sensors while performing various exercises.

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Article Source : PLOS Digital Health

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