Abstract
The efficiency of the electromyography signals acquired from the muscles is dependent on the processing of the signals. Factors such as electrode movements, electronic disturbances, and working environment often result in the addition of noise to the EMG signal acquired. To counter these noises; a wavelet denoising method is used in this paper. The denoised signals are subjected to time-domain feature extraction. The features used in this study are root mean square value, mean absolute value, integrated EMG, and waveform length. The features extracted are compared by using 3 different machine learning techniques. These are decision tree, linear discriminant analysis, and k-nearest neighbor (k-NN). The RMS feature tends to show the optimal classification result using the k-NN algorithm. Lastly; the pros and cons of the wavelet denoised signal are discussed by comparing it to the uncleaned signals.
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Khan, S.M., Khan, A.A., Farooq, O. (2021). Application of Wavelet Denoising for Phasic Classification in Pick and Place Task. In: Kumar, N., Tibor, S., Sindhwani, R., Lee, J., Srivastava, P. (eds) Advances in Interdisciplinary Engineering. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-9956-9_60
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DOI: https://doi.org/10.1007/978-981-15-9956-9_60
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