Journal of Korea Society of Industrial Information Systems (한국산업정보학회논문지)

Korea Society of Industrial Information Systems (한국산업정보학회)
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Design and Performance Analysis of ML Techniques for Finger Motion Recognition

손가락 움직임 인식을 위한 웨어러블 디바이스 설계 및 ML 기법별 성능 분석

  • 정우순 (대구대학교 대학원 정보통신공학과) ;
  • 이형규 (대구대학교 ICT융합학부)
  • Received : 2020.03.20
  • Accepted : 2020.04.22
  • Published : 2020.04.30
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Abstract

Recognizing finger movements have been used as a intuitive way of human-computer interaction. In this study, we implement an wearable device for finger motion recognition and evaluate the accuracy of several ML (Machine learning) techniques. Not only HMM (Hidden markov model) and DTW (Dynamic time warping) techniques that have been traditionally used as time series data analysis, but also NN (Neural network) technique are applied to compare and analyze the accuracy of each technique. In order to minimize the computational requirement, we also apply the pre-processing to each ML techniques. Our extensive evaluations demonstrate that the NN-based gesture recognition system achieves 99.1% recognition accuracy while the HMM and DTW achieve 96.6% and 95.9% recognition accuracy, respectively.

손가락 움직임 인식을 통한 제어는 직관적인 인간-컴퓨터 상호작용 방법의 하나이다. 본 연구에서는 여러 가지 ML (Machine learning) 기법을 사용하여 효율적인 손가락 움직임 인식을 위한 웨어러블 디바이스를 구현한다. 움직임 인식을 위한 시계열 데이터 분석에 전통적으로 사용되어 온 HMM (Hidden markov model) 및 DTW (Dynamic time warping) 기법뿐만 아니라 NN (Neural network) 기법을 적용하여 손가락 움직임 인식의 효율성 및 정확성을 비교하고 분석한다. 제안된 시스템의 경우, 경량화된 ML 모델을 설계하기 위해 각 ML 기법에 대해 최적화된 전처리 프로세스를 적용한다. 실험 결과, 최적화된 NN, HMM 및 DTW 기반 손가락 움직임 인식시스템은 각각 99.1%, 96.6%, 95.9%의 정확도를 제공한다.

Keywords

References

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