Using Myoelectric Signals to Classify Prehensile Patterns
Date
2016
Authors
Shuman, Gene R.
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Abstract
People want to live independently, but too often disabilities or advanced age robs them of the ability to do the necessary activities of daily living (ADLs). Finding relationships between electromyograms measured in the arm and movements of the hand and wrist needed to perform ADLs can help address performance deficits and be exploited in designing myoelectrical control systems for prosthetics and computer interfaces.
Description
Keywords
Computer science, Biomedical engineering, Artificial intelligence, Activities of daily living, Biomechanics, Classification, Electromyogram, Pattern recognition, Prehensile pattern