한국인터넷방송통신학회논문지 (The Journal of the Institute of Internet, Broadcasting and Communication)

  • 제17권2호
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  • Pages.179-190
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  • 2017
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  • 2289-0238(pISSN)
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  • 2289-0246(eISSN)
한국인터넷방송통신학회 (The Institute of Internet, Broadcasting and Communication)
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힐버트-황 변환에 통한 Hand Accelerometer 데이터의 핵심 패턴 추출

Applying Hilbert-Huang Transform to Extract Essential Patterns from Hand Accelerometer Data

  • 최병석 (동국대학교 정보통신공학과) ;
  • 서정열 (동국대학교 정보통신공학과)
  • 투고 : 2017.03.10
  • 심사 : 2017.04.07
  • 발행 : 2017.04.30
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초록

Hand Accelerometer는 인간신체 운동 패턴을 실시간으로 파악하는데 널리 사용되고 있다. 그러므로 행동 유형을 정확하게 파악하는 것은 아주 중요하다. 이 과정에서 각 행동유형의 형태를 미리 정확하게 파악하는 것이 중요하다. 인간의 신체 행동은 센서를 통해 수집된 시계열 데이터로 표현된다. 이 데이터는 비안정적, 비선형적 성격을 가지고 있다. 그래서 이런 성격의 데이터의 유형을 효율적으로 추출하는 방법을 찾는 것은 매우 중요하다. 힐버트-황 변환은 비안정적 비선형적 요소를 시계열데이터에서 효율적으로 추출하는 방법이다. 이 방법을 위의 시계열 데이터에 적용한 결과 핵심패턴이 성공적으로 추출되었다.

Hand Accelerometers are widely used to detect human motion patterns in real-time. It is essential to reliably identify which type of activity is performed by human subjects. This rests on having accurate template of each activity. Many human activities are represented as a set of multiple time-series data from such sensors, which are mostly non-stationary and non-linear in nature. This requires a method which can effectively extract patterns from non-stationary and non-linear data. To achieve such a goal, we propose the method to apply Hilbert-Huang Transform which is known to be an effective way of extracting non-stationary and non-linear components from time-series data. It is applied on samples of accelerometer data to determine its effectiveness.

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