한국산학기술학회논문지 (Journal of the Korea Academia-Industrial cooperation Society)

  • 제11권5호
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  • Pages.1564-1572
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  • 2010
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  • 1975-4701(pISSN)
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  • 2288-4688(eISSN)
한국산학기술학회 (The Korea Academia-Industrial cooperation Society)
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3축 가속도 센서를 이용한 낙상 검출 시스템 구현

Implementation of Falls Detection System Using 3-axial Accelerometer Sensor

  • 전아영 (부산대학교 대학원 의공학협동과정) ;
  • 유주연 (부산대학교 대학원 의공학협동과정) ;
  • 박근철 (부산대학교 대학원 의공학협동과정) ;
  • 전계록 (부산대학교 의학전문대학원 의공학교실)
  • Jeon, Ah-Young (Department of Interdisciplinary program in Biomedical engineering, Pusan National University) ;
  • Yoo, Ju-Yeon (Department of Interdisciplinary program in Biomedical engineering, Pusan National University) ;
  • Park, Geun-Chul (Department of Interdisciplinary program in Biomedical engineering, Pusan National University) ;
  • Jeon, Gye-Rok (Department of Biomedical engineering, School of Medicine, Pusan National University)
  • 투고 : 2010.04.01
  • 심사 : 2010.05.13
  • 발행 : 2010.05.31
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초록

본 연구에서는 3축 가속도 신호를 이용하여 낙상과 낙상 방향을 검출하는 시스템을 구현하였다. 가속도 신호는 3축 가속도 센서로부터 획득하였으며, 획득된 신호를 USB 인터페이스를 통하여 PC에 전달하였다. PC에 전송된 신호를 제안한 알고리즘을 사용하여 낙상을 검출하였으며, 퍼지 분류기를 사용하여 낙상의 방향을 분류하였다. 실험을 위하여 실험대상군 6명 선정하였으며, 가슴에 가속도계를 부착한 후 실험을 수행하였다. 실험대상자는 5초 동안 정상 보행을 한 후 4 가지 방향(전 후 좌 우)으로 낙상이 발생하도록 하였으며, 낙상에 소요되는 시간은 최소 2초로 설정하였다. 본 연구에서 제안된 알고리즘을 이용하여 낙상을 검출하였으며 낙상 발생 후 1초부터 데이터를 분석하고 퍼지 분류기를 이용하여 낙상방향을 분류하였다. 낙상 검출율은 평균 94.79%이었다. 낙상 방향에 따른 분류율은 front_fall은 95.83%, back_fall은 100%, left_fall 은 87.5%, right_fall은 95.83%이었다.

In this study, the falls detection and direction classification system was implemented using 3-axial acceleration signal. The acceleration signals were acquired from the 3-axial accelerometer(MMA7260Q, Freescale, USA), and then transmitted to the computer through USB interface. The implemented system can detect falls using the newly proposed algorithm, and also classify the direction of falls using fuzzy classifier. The 6 subjects was selected for experiment and the accelerometer was attached on each subject's chest. Each subject walked in normal pace for 5 seconds, and then the fall down according to the four direction(front_fall, back_fall, left_fall and right_fall) during at least 2 second. The falls was easily detect using the newly proposed algorithm in this study. The acquired signals were analyzed after 1 second from generating falls. The fuzzy classifier was used to classify the direction of falls. The mean value of the falls detection rate was 94.79%. The classifier rate according to falls direction were 95.83% in case of front falls, 100% incase of back falls, 87.5% in case of left falls, and 95.83% in case of right falls.

키워드

참고문헌

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피인용 문헌

  1. Determination of Fall Direction Before Impact Using Support Vector Machine vol.24, pp.1, 2015, https://doi.org/10.5369/JSST.2015.24.1.47