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Home / Archives / Volume-6 / Issue-3 / Article-2

Volume - 6 | Issue - 3 | september 2024

Classification of Electromyographic Hand Gesture Signal using Deep Learning Open Access
Maitreyi Rajaraman  , Sarojini Premalatha J.  84
Pages: 239-252
Cite this article
Rajaraman, Maitreyi, and Sarojini Premalatha J.. "Classification of Electromyographic Hand Gesture Signal using Deep Learning." Journal of Soft Computing Paradigm 6, no. 3 (2024): 239-252
Published
23 July, 2024
Abstract

The electromyography (EMG) signal measures the electrical activity of muscles and is often described as a function of amplitude, frequency, and phase over time. These signals are commonly employed in both clinical and biomedical applications. They are used to identify neuromuscular disorders and in other activities such as controlling robots and computers. This proposed study utilizes the CNN to analyse the hand gestures and extract valuable information from these gestures. Consecutive training and testing using images were conducted to evaluate the CNN's performance. The findings demonstrate the effectiveness of the proposed methodology in discerning significant features from complex movements.

Keywords

Convolutional Neural Network Sign Language Recognition Hand Gestures Image Processing EMG

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