센서학회지 (Journal of Sensor Science and Technology)

  • 제31권2호
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  • Pages.96-101
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  • 2022
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  • 1225-5475(pISSN)
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  • 2093-7563(eISSN)
한국센서학회 (The Korean Sensors Society)
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IMU 원신호 기반의 기계학습을 통한 충격전 낙상방향 분류

Classification of Fall Direction Before Impact Using Machine Learning Based on IMU Raw Signals

  • 이현빈 (한경대학교 기계공학과) ;
  • 이창준 (한경대학교 기계공학과) ;
  • 이정근 (한경대학교 ICT 로봇기계공학부)
  • Lee, Hyeon Bin (Mechanical Engineering, Hankyong National University) ;
  • Lee, Chang June (Mechanical Engineering, Hankyong National University) ;
  • Lee, Jung Keun (School of ICT, Robotics & Mechanical Engineering, Hankyong National Unversity)
  • 투고 : 2022.01.18
  • 심사 : 2022.03.18
  • 발행 : 2022.03.31
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초록

As the elderly population gradually increases, the risk of fatal fall accidents among the elderly is increasing. One way to cope with a fall accident is to determine the fall direction before impact using a wearable inertial measurement unit (IMU). In this context, a previous study proposed a method of classifying fall directions using a support vector machine with sensor velocity, acceleration, and tilt angle as input parameters. However, in this method, the IMU signals are processed through several processes, including a Kalman filter and the integration of acceleration, which involves a large amount of computation and error factors. Therefore, this paper proposes a machine learning-based method that classifies the fall direction before impact using IMU raw signals rather than processed data. In this study, we investigated the effects of the following two factors on the classification performance: (1) the usage of processed/raw signals and (2) the selection of machine learning techniques. First, as a result of comparing the processed/raw signals, the difference in sensitivities between the two methods was within 5%, indicating an equivalent level of classification performance. Second, as a result of comparing six machine learning techniques, K-nearest neighbor and naive Bayes exhibited excellent performance with a sensitivity of 86.0% and 84.1%, respectively.

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과제정보

본 연구는 2018년도 정부(교육부)의 재원으로 한국연구재단 기초연구사업(No. 2018R1D1A1B07042791)의 지원을 받아 수행됨.

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