인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
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
- 2018.1
- 수록면
- 716 - 720 (5page)
이용수
초록· 키워드
The aim of this study is to build human activity recognition (HAR) model using deep neural network (DNN) and investigate the influence that affects misclassification. As wearable devices become widespread and used in various applications such as health care and sports, people are interested in HAR. Therefore, it is important to improve classification performance in HAR. We implemented a DNN based HAR model through open smartphone sensor data set and t-Distributed Stochastic Neighbor Embedding was used to visualize extracted features. The performance of the DNN model was excellent except for one activity. Through the visualization of the extracted features, we were able to identify the cause of the performance degradation. Similar extracted features between activities are the cause of performance degradation. The DNN model can recognize human activity using smart phone sensor data and be used for health care, sports, fall detection and so on.
#Human Activity Recognition
#Smart sensor
#Deep neural network
#t-Distributed Stochastic Neighbor Embedding
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목차
- 요약문
- 1. 연구배경
- 2. 연구방법
- 3. 연구결과
- 4. 결론
- 참고문헌
참고문헌
참고문헌 신청최근 본 자료
UCI(KEPA) : I410-ECN-0101-2018-004-001761610