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선박 탑승자를 위한 다중 센서 기반의 스마트폰을 이용한 활동 인식 시스템

라지브 쿠마 피야레;이성로
Piyare, Rajeev Kumar;Lee, Seong Ro

  • 투고 : 2014.05.08
  • 심사 : 2014.09.12
  • 발행 : 2014.09.30

초록

상황 인식은 유비쿼터스컴퓨팅 환경에 대한 진화를 변화시켰고 무선 센서네트워크 기술은 많은 응용기기에 대한 새로운 방법을 제시하였다. 특히, 행동 인식은 사람의 응용서비스를 제공하는데 있어 특정 사용자의 상황을 인식하는 핵심 요소로 의학, 취미, 군사 분야에서 폭넓은 응용분야를 갖고 있고 사용반경의 확대에서도 효율과 정확도를 높이는 방법에 크게 기여한다. 스마트폰 센서로부터 나오는 데이터로부터 프레임이 512인셈플 데이터를 얻어, 프레임간50%의 오버랩을 갖도록 하고 Machine Learning Algorithm 인 WEKA Experimenter (University of Waikato, Version 3.6.10)을 써서 데이더로부터 시간영역 특징값을 추출함으로써 행동 인식에 대한 99.33%의 정확도를 얻을 수 있었다. 또한, WEKA Experimenter의 사용기법인 C4.5 Decision Tree과 다른 방법인 BN, NB, SMO or Logistic Regression간의 비교실험을 하였다.

키워드

sensor fusion;activity recognition;classification algorithms;feature extraction

참고문헌

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피인용 문헌

  1. Implementation of Smart Convergent Communication System of Satellite and Wireless for Monitoring in Closed Room of Vessel vol.19, pp.8, 2015, https://doi.org/10.7840/kics.2014.39C.9.811

과제정보

연구 과제 주관 기관 : 한국연구재단