The strength of the muscle contraction can be easily measured by the muscle activity extracted at the skin surface. Analysis of surface Electromyogram (sEMG) is one of the standard procedures to identify posture, gesture and actions (i.e. control of prosthe- sis via learnt body actions). sEMG signals are usually complex in nature. It can be easily classified into differentiated muscular activities with appropriate signal processing tools. In order to analyze its complexity, various studies have been carried out but have proved unsuccessful, due to huge differences in muscular activities of some muscles over the other. This paper presents a new technique to identify low level hand movement by classifying the single channel sEMG. Single channel sEMG analysis is preferred over multi-channel due to its simplicity, computational cost and efficiency. Wavelet transformation and artificial neural network (ANN) classifier are utilized to classify and analyze the sEMG signal in a better way.
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