Abstract
Finger joint angle pattern recognition is significant for the development of an intelligent bionic hand. It makes the intelligent prosthesis understand the users intension more accurately and complete movements better. Surface electromyography signals have been widely used in intelligent bionics prosthesis research and rehabilitation medicine due to its advantages like high efficiency, convenient collection and non-invasive access. An improved grid-search method using a support vector machine has been proposed for the finger joint angle pattern recognition issue in surface electromyography signals. Pattern recognition for surface electromyography signals of index finger movement and metacarpophalangeal joint angle has been performed. Better classification performance was achieved through screening of feature vector combined with an improved grid-search support vector machine classification algorithm.
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