디지털콘텐츠학회 논문지 (Journal of Digital Contents Society)

  • 제19권9호
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  • Pages.1779-1785
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  • 2018
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  • 1598-2009(pISSN)
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  • 2287-738X(eISSN)
한국디지털콘텐츠학회 (Digital Contents Society)
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스마트 기기를 이용한 실시간 상황인식의 오차 보정

Error Correction of Real-time Situation Recognition using Smart Device

  • 김태호 (충북대학교 전기전자정보컴퓨터학부 데이터베이스/바이오인포매틱스연구실) ;
  • 서동혁 (단국대학교 디스플레이공학과) ;
  • 윤신숙 (남서울대학교 전자공학과) ;
  • 류근호 (충북대학교 전기전자정보컴퓨터학부 데이터베이스/바이오인포매틱스연구실)
  • Kim, Tae Ho (Database/Bioinformatics Lab, School of Electrical & Computer Engineering, Chungbuk National University) ;
  • Suh, Dong Hyeok (Department of Display Engineering, Dankook University) ;
  • Yoon, Shin Sook (Department of Electronic, Namseoul University) ;
  • Ryu, KeunHo (Database/Bioinformatics Lab, School of Electrical & Computer Engineering, Chungbuk National University)
  • 투고 : 2018.09.05
  • 심사 : 2018.09.27
  • 발행 : 2018.09.30
⟨ 이전 논문 다음 논문 ⟩

초록

본 연구에서는 사물인터넷 기술을 이용하는 스마트 웨어러블 기기의 상황인식 기능을 향상시키기 위하여 센서부의 이벤트 데이터에 대한 오차 보정 방안을 제안하였다. 스마트 기기를 통한 상황인식에서 기기의 특성상 필수적인 상황 정보 센싱을 함에 있어서 오차가 불가피하게 발생하고, 이는 예측 성능을 저하시키는 요인이 된다. 이러한 문제를 해결하기 위하여 본 연구에서는 칼만필터의 오류보정 알고리즘을 적용하여 스마트기기의 3축 가속도 센서에서 입수되는 신호 값을 보정하였다. 결과적으로 시계열 데이터를 이루는 3축 가속도 센서가 감지하여 보고하는 데이터에 대한 처리 과정에서 발생하는 오차를 칼만필터를 통하여 효과적으로 제거할 수 있었다. 이 연구가 차후 개발되어질 실시간 상황인지 시스템의 성능을 향상시켜 줄 수 있을 것이라 기대한다.

In this paper, we propose an error correction method to improve the accuracy of human activity recognition using sensor event data obtained by smart devices such as wearable and smartphone. In the context awareness through the smart device, errors inevitably occur in sensing the necessary context information due to the characteristics of the device, which degrades the prediction performance. In order to solve this problem, we apply Kalman filter's error correction algorithm to compensate the signal values obtained from 3-axis acceleration sensor of smart device. As a result, it was possible to effectively eliminate the error generated in the process of the data which is detected and reported by the 3-axis acceleration sensor constituting the time series data through the Kalman filter. It is expected that this research will improve the performance of the real-time context-aware system to be developed in the future.

키워드

참고문헌

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