인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
개인구독
소속 기관이 없으신 경우, 개인 정기구독을 하시면 저렴하게
논문을 무제한 열람 이용할 수 있어요.
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
피인용 0
초록·키워드
Objective: The aim of this study is to propose an algorithm to distinguish specific behaviors of the elderly using IMU sensor. Background: In recent years, many wearable devices are being developed. In addition, as the aging society progresses, it is emphasized the necessity of development of a wearable devices for the elderly living alone, who is likely to be in an emergency during daily life. Therefore, the need to build a database for the elderly is emerging. Analysis of human activities can offer useful information regarding on individual’s degree of functional ability and lifestyle, especially for the elderly. Method: Four male and six female volunteers (average age: 72.4±3.2years; height: 164.1±7.5㎝; weight: 63.5±11.8㎏g) participated in the experiment. The minimum number of IMU sensors for the whole body motion classification was set to five. And the specific action was set to three actions: lying down, sitting down, sitting in a chair. To The signal vector magnitude (SVM) acquired from angular velocity data of sensor was used to distinguish specific postures. Results: Data of sensors which are located on upper arm, lower thoracic and pelvic showed no significant differences in three actions. However, the proposed algorithm is possible to distinguish three postures using the rest sensors. Conclusion: Motion classification algorithm based on angular velocity with only two sensors was applicable to distinguish motion of lying down, sitting down, and sitting in a chair. Application: The results of our study might help to build a database of wearable device on specific behaviors of the elderly.
본문·목차
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UCI(KEPA) : I410-ECN-0101-2018-530-001741555
