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Recognition of hand gestures with different prior postures using EMG signals

사전 자세에 따른 근전도 기반 손 제스처 인식

  • Hyun-Tae Choi (Artificial Intelligence and Convergence Department, Pukyong National University) ;
  • Deok-Hwa Kim (Department of Computer Engineering, Pukyong National University) ;
  • Won-Du Chang (Division of Computer Engineering and Artificial Intelligence, Pukyong National University)
  • 최현태 (부경대학교 인공지능융합학과) ;
  • 김덕화 (부경대학교 컴퓨터공학과) ;
  • 장원두 (부경대학교 컴퓨터.인공지능공학부)
  • Received : 2023.09.20
  • Accepted : 2023.11.20
  • Published : 2023.12.31

Abstract

Hand gesture recognition is an essential technology for the people who have difficulties using spoken language to communicate. Electromyogram (EMG), which is often utilized for hand gesture recognition, is expected to have difficulties in hand gesture recognition because its people's movements varies depending on prior postures, but the study on this subject is rare. In this study, we conducted tests to confirm if the prior postures affect on the accuracy of gesture recognition. Data were recorded from 20 subjects with different prior postures. We achieved average accuracies of 89.6% and 52.65% when the prior states between the training and test data were unique and different, respectively. The accuracy was increased when both prior states were considered, which confirmed the need to consider a variety of prior states in hand gesture recognition with EMG.

손 제스처의 인식은 구어 사용이 어려운 사람들의 의사소통을 위한 중요한 기술이다. 제스처 인식에 널리 사용되는 근전도 신호는 사전 자세에 따라 동작이 달라지기 때문에 제스처 인식의 어려움이 있을 것으로 예상되지만, 이에 관한 연구는 찾기 어렵다. 본 연구에서는 사전 자세에 따른 제스처 인식 성능의 변화를 분석하였다. 이를 위해 총 20명의 피험자에게서 사전 자세를 가지는 동작에 대한 근전도 신호를 측정하고, 제스처 인식을 실험하였다. 그 결과, 학습 및 테스트 데이터 간 사전 상태가 단일한 경우에는 평균 89.6%의 정확도를, 상이한 경우에는 평균 52.65%의 정확도를 보였다. 반면, 사전 자세를 모두 고려한 경우에는 정확도가 다시 회복됨을 발견하였다. 이를 통해 본 연구에서는 근전도를 활용하는 손 제스처 인식시에 사전 자세가 다양하게 고려하여야 함을 실험적으로 확인하였다.

Keywords

Acknowledgement

본 논문은 부경대학교 자율창의학술연구비(2022년)에 의하여 연구되었음.

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