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| Study on Wrists MultiMovement Pattern Recognition Based on sEMG |
| Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou 310018, China |
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Abstract Action pattern recognition of limbs using sEMG is the basis for bionic control of a prosthetic hand. In consideration of the generation mechanism of sEMG, the approximate entropy and the fractal dimension, which feature the sEMG's morphological characteristics including complexity and overall selfsimilarity, was chosen as the feature vector of pattern recognition to improve action mode recognition rate. In the meantime, a K nearest neighbor (KNN) model incremental learning method with incremental learning ability, was presented as a classifier of pattern recognition. In pattern recognition experiment of classifying four fine movements of the wrist (namely wrist extension, wrist flexion, wrist pronation, wrist supination) with 10 participants, the correct mode recognition rate is above 92.5%. In a contrast experiment that was designed to evaluate the effects of the increment learning ability to the action mode recognition rate, the correct recognition rate is 4.5 percent higher than KNN mode arithmetic without incremental learning ability when the prosthetic users changed physiologically. The above experimental result shows action mode recognition method based on the EMG is reasonable and practical.
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