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.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2014.10
- 수록면
- 540 - 545 (6page)
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초록· 키워드
오류제보하기
This paper presents a bed-leaving detection method using Elman-type Counter Propagation Networks (ECPNs), a novel machine-learning-based method used for time-series signals. In our earlier study, we used CPNs, a form of supervised model of Self-Organizing Maps (SOMs), to produce category maps to learn relations among input and teaching signals. For this study, we inserted a feedback loop as the second Grossberg layer for learning time-series features. Moreover, we developed an original caster-stand sensor using piezoelectric films to measure weight changes of a subject on a bed to be loaded through bed legs. The features of our sensor are that it obviates a power supply for operations and that it can be installed on existing beds. We evaluated our sensor system by examining 10 people in an environment representing a clinical site. The mean recognition accuracy for seven behavior patterns is 71.1%. Furthermore, the recognition accuracy for three behavior patterns of sleeping, sitting, and leaving the bed is 83.6% Falsely recognized patterns remained inside of respective categories of sleeping and sitting. We infer that this system is applicable to an actual environment as a novel sensor system requiring no restraint of patients.
목차
Abstract
1. INTRODUCTION
2. SENSOR SYSTEM
3. RECOGNITION METHODS
4. EXPERIMENTAL RESULTS
5. CONCLUSION
REFERENCES
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