Artificial Neural Network based Body Posture Classification from EMG signal analysis

Rajesh Kumar Tripathy, Ashutosh Acharya, Sumit Kumar Choudhary, Santosh Kumar Sahoo



This paper deals with the body posture Classification from EMG signal analysis using artificial neural network (ANN). The various statistical features extracted from each EMG signal corresponding to different muscles associated with the different body postures are framed using LABVIEW software. Further-more, these features are taken as the input towards the ANN classifier and thus the corresponding output for the respective classifier predicts the postures like Bowing, Handshaking, and Hugging. The performance of the classifier is determined by the classification rate (CR). The outcome of result indicates that the CR of Multilayer Feed Forward Neural Network (MFNN) type of ANN is rounded up to a percentage of 71.02%.


EMG, statistical features, LABVIEW,ANN, MFNN, CR

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Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
ISSN 2089-3272

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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