Classification of Surface Electromyogram Signals Acquired from the Forearm of a Healthy Volunteer

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Author(s): Uvanesh K. (National Institute of Technology, Rourkela, India), Suraj Kumar Nayak (National Institute of Technology, Rourkela, India), Biswajeet Champaty (National Institute of Technology, Rourkela, India), Goutam Thakur (Manipal Institute of Technology, India), Biswajit Mohapatra (Vesaj Patel Hospital, India), D. N. Tibarewala (School of BioScience and Engineering, Jadavpur University, India)and Kunal Pal (National Institute of Technology, Rourkela, India)
Copyright: 2016
Pages: 19
Source title: Classification and Clustering in Biomedical Signal Processing
Source Author(s)/Editor(s): Nilanjan Dey (Techno India College of Technology, India)and Amira Ashour (Tanta University, Egypt)
DOI: 10.4018/978-1-5225-0140-4.ch013

Keywords: Computer Science & IT / Computer Vision & Image Processing / Medical Information Science Reference / Medical Technologies

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Abstract

Surface EMG (sEMG) signals from the palmaris longus, flexor carpi radialis and flexor carpi ulnaris muscles were recorded using an in-house developed EMG signal acquisition system. The bandwidth of the acquisition system was 1500 Hz. The extracted sEMG signal was processed using Discrete Wavelet Transform (DWT). The features of the extracted and the wavelet processed signals were determined and were used for probable classification using Artificial Neural Network (ANN). A classification efficiency of more than 90% was achieved using ANN classifiers. The results suggested that the sEMG may be successfully used for designing efficient control system.

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Classification of Surface Electromyogram Signals Acquired from the Forearm of a Healthy Volunteer

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