메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색

논문 기본 정보

자료유형
학술저널
저자정보
저널정보
한국센서학회 센서학회지 센서학회지 제26권 제1호
발행연도
2017.1
수록면
7 - 14 (8page)

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색

초록· 키워드

오류제보하기
A threshold-based fall recognition algorithm using a tri-axial accelerometer and a bi-axial gyroscope mounted on the skin above the upper sternum was proposed to recognize fall-like activities of daily living (ADL) events. The output signals from the tri-axial accel-erometer and bi-axial gyroscope were obtained during eight falls and eleven ADL action sequences. The thresholds of signal vectormagnitude (SVM_Acc), angular velocity (ωres), and angular variation (θres) were calculated using MATLAB. When the measured valuesof SVM_Acc, ωres, and θres were compared to the threshold values (TH1, TH2, and TH3), fall-like ADL events could be distinguished from a fall. When SVM_Acc was larger than 2.5 g (TH1), ωres was larger than 1.75 rad/s (TH2), and θres was larger than 0.385 rad (TH3), eight falls and eleven ADL action sequences were recognized as falls. When at least one of these three conditions was not satisfied,the action sequences were recognized as ADL. Fall-like ADL events such as jogging and jumping up (or down) have posed a problemin distinguishing ADL events from an actual fall. When the measured values of SVM_Acc, ωres, and θres were applied to the sequentialprocessing algorithm proposed in this study, the sensitivity was determined to be 100% for the eight fall action sequences and the spec- ificity was determined to be 100% for the eleven ADL action sequences.

목차

등록된 정보가 없습니다.

참고문헌 (19)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0