재활복지공학회논문지 (Journal of rehabilitation welfare engineering & assistive technology)

한국재활복지공학회 (Rehabilitation Engineering And Assistive Technology Society of Korea)
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근전도와 관성센서가 내장된 암밴드를 이용한 실시간 수화 인식

Real-time Sign Language Recognition Using an Armband with EMG and IMU Sensors

  • 김성중 (연세대학교 보건과학대학 의공학부) ;
  • 이한수 (연세대학교 보건과학대학 의공학부) ;
  • 김종만 (연세대학교 의공학과) ;
  • 안순재 (연세대학교 의공학과) ;
  • 김영호 (연세대학교 의공학과)
  • 투고 : 2016.11.07
  • 심사 : 2016.11.29
  • 발행 : 2016.11.30
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초록

수화를 사용하는 농아인은 의사소통의 제약에 의해 사회적인 불평등과 금전적 손실을 겪고 있다. 이러한 이유로 본 연구에서는 농아인의 원활한 의사소통을 위해 8개의 근전도와 1개의 관성센서로 구성된 암밴드 센서를 이용하여 실시간으로 미국 수화를 인식하는 알고리즘을 개발하였다. 개발된 알고리즘의 성능 검증은 11명의 피험자를 통해 진행하였으며, 패턴 분류기 학습은 훈련 데이터베이스 크기를 증가시키면서 진행하였다. 실험 결과, 개발된 패턴 인식 알고리즘은 동작 별 20개의 훈련 데이터베이스에서 97%이상의 정확도를 가졌으며, 30개의 훈련 데이터베이스에서 99%이상의 정확도를 보였다. 이를 통해 본 연구에서 제안하는 암밴드 센서를 이용한 수화 인식 알고리즘의 실용성과 우수성을 확인하였다.

Deaf people using sign language are experiencing social inequalities and financial losses due to communication restrictions. In this paper, real-time pattern recognition algorithm was applied to distinguish American Sign Language using an armband sensor(8-channel EMG sensors and one IMU) to enable communication between the deaf and the hearing people. The validation test was carried out with 11 people. Learning pattern classifier was established by gradually increasing the number of training database. Results showed that the recognition accuracy was over 97% with 20 training samples and over 99% with 30 training samples. The present study shows that sign language recognition using armband sensor is more convenient and well-performed.

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참고문헌

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