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A sEMG Classification Framework with Less Training Data

Supervised machine learning algorithms, such as Artificial Neural Network (ANN), have been applied to surface electromyograph (sEMG) to classify user’s muscular states. This paper introduces a novel framework to design a binary sEMG classifier to distinguish if the user performs a repetitive motion with a dumbbell. This framework enables to reduce the number of tasks required for collecting training data as it utilizes prior knowledge of sEMG. The performance of the proposed classifier is validated experimentally. Experimental results show that the proposed framework enables the design of a classifier which distinguishes the user’s state with a 95.7% success rate. This accuracy is comparable to an accuracy of ANN classifier (99.6%), but with less training data. Under the identical training conditions, the accuracy of the proposed framework outperforms the ANN classifier whose accuracy drops to 65.6%.