Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Classification of Gripping Movement in Daily Life Using EMG-based Spider Chart and Deep Learning

근전도 기반의 Spider Chart와 딥러닝을 활용한 일상생활 잡기 손동작 분류

  • Lee, Seong Mun (Department of Biomedical Engineering, Konyang University) ;
  • Pi, Sheung Hoon (Department of Biomedical Engineering, Konyang University) ;
  • Han, Seung Ho (Department of Biomedical Engineering, Konyang University) ;
  • Jo, Yong Un (Department of Biomedical Engineering, Konyang University) ;
  • Oh, Do Chang (Department of Biomedical Engineering, Konyang University)
  • Received : 2022.08.10
  • Accepted : 2022.10.05
  • Published : 2022.10.31
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Abstract

In this paper, we propose a pre-processing method that converts to Spider Chart image data for classification 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 training, 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.

Keywords

Acknowledgement

본 연구는 2021년도 정부(과학기술정보통신부)의 재원으로 한국연구재단 기초연구사업의 과제의 지원을 받아 수행하였음(No. 2021R1I1A304391111).

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