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KMID : 1137820220430050299
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의공학회지
2022 Volume.43 No. 5 p.299 ~ p.307
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Classification of Gripping Movement in Daily Life Using EMG-based Spider Chart and Deep Learning
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Lee Seong-Mun
Pi Sheung-Hoon
Han Seung-Ho
Jo Yong-Un
Oh Do-Chang
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Abstract
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In this paper, we propose a pre-processing method that converts to Spider Chart image data for classi- fication of gripping movement using EMG (electromyography) sensors and Convolution Neural Networks (CNN) deep learning. First, raw data for six hand gestures are extracted from five test subjects using an 8-channel armband and converted into Spider Chart data of octagonal shapes, which are divided into several sliding windows and are learned. In classifying six hand gestures, the classification performance is compared with the proposed pre-processing method and the existing methods. Deep learning was performed on the dataset by dividing 70% of the total into train- ing, 15% as testing, and 15% as validation. For system performance evaluation, five cross-validations were applied by dividing 80% of the entire dataset by training and 20% by testing. The proposed method generates 97% and 94.54% in cross-validation and general tests, respectively, using the Spider Chart preprocessing, which was better results than the conventional methods.
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KEYWORD
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Convolution neural network, Electromyography, Pre-processing, Hand motion, Spider chart
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