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논문 기본 정보

자료유형
학술대회자료
저자정보
(서울대학교) (서울대학교) (서울대학교) (서울대학교)
저널정보
한국HCI학회 한국HCI학회 학술대회 PROCEEDINGS OF HCI KOREA 2018 학술대회 발표 논문집
발행연도
수록면
716 - 720 (5page)

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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.
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목차

  1. 요약문
  2. 1. 연구배경
  3. 2. 연구방법
  4. 3. 연구결과
  5. 4. 결론
  6. 참고문헌

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UCI(KEPA) : I410-ECN-0101-2018-004-001761610