전자공학회논문지 (Journal of the Institute of Electronics and Information Engineers)

  • 제52권1호
  • /
  • Pages.162-172
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  • 2015
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  • 2287-5026(pISSN)
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  • 2288-159X(eISSN)
대한전자공학회 (The Institute of Electronics and Information Engineers)
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진동센서 기반 걸음걸이 검출 및 분류 알고리즘

Footstep Detection and Classification Algorithms based Seismic Sensor

  • 강윤정 (한국과학기술원 해양시스템공학전공) ;
  • 이재일 (제주대학교 해양시스템공학과) ;
  • 배진호 (제주대학교 해양시스템공학과) ;
  • 이종현 (제주대학교 해양시스템공학과)
  • Kang, Youn Joung (Division of Ocean Systems Engineering, KAIST) ;
  • Lee, Jaeil (Dept. of Ocean System Engineering, Jeju Nat'l University) ;
  • Bea, Jinho (Dept. of Ocean System Engineering, Jeju Nat'l University) ;
  • Lee, Chong Hyun (Dept. of Ocean System Engineering, Jeju Nat'l University)
  • 투고 : 2014.07.28
  • 심사 : 2014.12.30
  • 발행 : 2015.01.25
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초록

본 논문에서는 적응형 걸음걸이 검출 알고리즘과 검출된 신호로부터 단일 발자국의 움직임을 분류하는 알고리즘을 제안한다. 제안된 단일 발자국 기반 알고리즘은 기존의 연속된 발자국 신호를 이용한 분류 방식이 아니기 때문에 전체적인 움직임뿐만 아니라 개별적이고 불규칙한 움직임도 검출 및 분류 가능하다. 분류를 위해 사용된 특징벡터는 발자국 신호의 푸리에 스펙트럼, CWT의 스펙트럼, AR 모델링 스펙트럼과 AR 스펙트로그램 영상으로부터 얻어진 벡터이다. SVM을 이용하여 단일 발자국의 움직임을 분류한 결과 AR 스펙트로그램으로 얻어진 특징벡터를 사용할 경우 90% 이상 분류 성능을 얻었다.

In this paper, we propose an adaptive detection algorithm of footstep and a classification algorithm for activities of the detected footstep. The proposed algorithm can detect and classify whole movement as well as individual and irregular activities, since it does not use continuous footstep signals which are used by most previous research. For classifying movement, we use feature vectors obtained from frequency spectrum from FFT, CWT, AR model and image of AR spectrogram. With SVM classifier, we obtain classification accuracy of single footstep activities over 90% when feature vectors using AR spectrogram image are used.

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참고문헌

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