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Forecasting the Semg Signal Using Wavelet Transform and Anfis Model

  • RESEARCH ARTICLE
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Proceedings of the National Academy of Sciences, India Section A: Physical Sciences Aims and scope Submit manuscript

Abstract

In this paper we study how the muscles in the human body move, electromyography (EMG Signal) is employed as a diagnostic technique for identifying various muscular activity. Noise from the SEMG signal is effectively minimized with a suitable wavelet selection. The root mean square values have been evaluated to determine which wavelet is the most efficient for signal denoising. Further, since a learning method of a neural structure with connections based on rules is necessary to be able to estimate the relationship, this paper also aims to analyse an approach that uses signals obtained by surface electrodes to characterize hand movements of the human arm for pattern recognition (i.e. ANFIS method is employed). The characteristics of seven hand gestures are categorized using the ANFIS-based learning, which is then assessed in order to predict the link between input and output.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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  1. Department of Computer Science and Engineering, DR BR Ambedkar National Institute of Technology, Jalandhar, Punjab, India

    Tanu Sharma & K. P. Sharma

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  1. Tanu Sharma
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  2. K. P. Sharma
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Correspondence to Tanu Sharma.

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The study has been performed in accordance (reference no. NITJ/IC/2021696) with the declaration of Helsinki and consent was taken prior to conduct experiment and data is available with corresponding author.

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Sharma, T., Sharma, K.P. Forecasting the Semg Signal Using Wavelet Transform and Anfis Model. Proc. Natl. Acad. Sci., India, Sect. A Phys. Sci. 94, 213–225 (2024). https://doi.org/10.1007/s40010-024-00877-9

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