Abstract
This paper details a strategy of discriminating grasp types using surface electromyogram (EMG) signals, which has the potential to be applied for controlling extreme upper limb prosthesis. We have investigated the recognition of six grasp types used during 70% of daily living activities based on two-channel EMG. A grasp classification architecture and feature set have been proposed through the iterative development of the feature set as well as the classifier. Three different classifiers and a variety of features have been explored. From the experimental results, we have hypothesised that continuous wavelet transform function coefficients of the EMG signals having entropy values close to the entropy values of preprocessed EMG signals possess maximum informations about the grasp types. Further, sum of discrete wavelet transform coefficients of EMG signals has been established as a primal feature for grasp classification.
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