Analysis and recognition of operations using SEMG from upper arm muscles

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Abstract
Accurate muscular force estimation (from upper arm muscles) based on surface electromyogram forms an important issue in upper limb prosthetic design applications. The whole system consists of surface electrodes, signal acquisition protocols, and signal conditioning at different levels. Labview soft scope was used to acquire the surface electromyogram signal from the designed hardware. The study is concerned with the estimation of characteristics of recorded signals, and for that, statistical techniques of PCA were exercised for verifying the effectiveness of the processed signal against different upper arm motions before its classification. Thereafter, artificial neural network classifier was implemented for the classification surface electromyogram signals with best classification rate of 89.30%. Finally, the processing technique was used to significantly (p < .05) improve classification rate, without much loss of information.
Funding Information
  • Ministry of Human Resource Development

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