Event Title

The Design and Effect of a Wearable Myoelectric Computer Interface to Reduce Abnormal Co-Activation After Stroke

Session Number

Project ID: MEDH 31

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Advisor(s)

Dr. Marc Slutzky; Northwestern Feinberg School of Medicine

Discipline

Medical and Health Sciences

Start Date

22-4-2020 11:00 AM

End Date

22-4-2020 11:25 AM

Abstract

Abnormal co-activation, the incapacity to independently activate one’s muscles, has been identified as a significant factor behind upper extremity impairment of the arm after stroke. In a previous study, we developed a myoelectric computer interface (MyoCI) training paradigm, which maps electromyographic (EMG) signals to cursor movements, to help train stroke survivors to reduce abnormal co-activation. This study found the paradigm potentially effective as a tool for chronic stroke rehabilitation. We then modified this original paradigm into four paradigms for distinct subject groups: 1) the original group, providing feedback for two muscles, 2) a control group, providing only a single muscle feedback, 3) a group providing feedback for three muscles, and 4) a group instructing players to reach out with their arm during training. We are evaluating these four paradigms with both acute and chronic stroke survivors, measuring functional scores as well as performance within the training software. So far, in chronic participants, the 3-muscle group has produced greater gains than the original paradigm group, and all groups have outperformed the control. Our current results support the original paradigm and the 3-muscle paradigm as effective tools to reduce abnormal co-activation in chronic stroke survivors, while the reach group requires more evidence to make such a distinction.

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Apr 22nd, 11:00 AM Apr 22nd, 11:25 AM

The Design and Effect of a Wearable Myoelectric Computer Interface to Reduce Abnormal Co-Activation After Stroke

Abnormal co-activation, the incapacity to independently activate one’s muscles, has been identified as a significant factor behind upper extremity impairment of the arm after stroke. In a previous study, we developed a myoelectric computer interface (MyoCI) training paradigm, which maps electromyographic (EMG) signals to cursor movements, to help train stroke survivors to reduce abnormal co-activation. This study found the paradigm potentially effective as a tool for chronic stroke rehabilitation. We then modified this original paradigm into four paradigms for distinct subject groups: 1) the original group, providing feedback for two muscles, 2) a control group, providing only a single muscle feedback, 3) a group providing feedback for three muscles, and 4) a group instructing players to reach out with their arm during training. We are evaluating these four paradigms with both acute and chronic stroke survivors, measuring functional scores as well as performance within the training software. So far, in chronic participants, the 3-muscle group has produced greater gains than the original paradigm group, and all groups have outperformed the control. Our current results support the original paradigm and the 3-muscle paradigm as effective tools to reduce abnormal co-activation in chronic stroke survivors, while the reach group requires more evidence to make such a distinction.