Stream data analysis of body sensors for sleep posture monitoring: An automatic labelling approach

Poyuan Jeng, Li-Chun Wang

研究成果: Conference contribution

3 引文 斯高帕斯(Scopus)

摘要

Sleeping is one of the most important activities in our daily lives. However, very few people really understand their sleeping habits, which affect sleep-related diseases such as sleep apnea, back problems or even snoring. Most current techniques that monitor, predict and quantify sleep postures are limited to use in hospitals and/or need the intervention of caregivers. In this paper, we describe a system to automatically monitor, predict and quantify sleep postures that may be self-applied by the general public even in a non-hospital environment such as at a persons home. A Random Forest approach is adopted during training to predict and quantify sleep postures. After going through training procedures, a person needs only one sensor placed on the wrist to recognize the persons sleep postures. Our preliminary experiments using a set of testing data show about 90 percent accuracy, indicating that this design has a promising future to accurately analyze, predict and quantify human sleep postures.

原文English
主出版物標題2017 26th Wireless and Optical Communication Conference, WOCC 2017
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781509049097
DOIs
出版狀態Published - 15 五月 2017
事件26th Wireless and Optical Communication Conference, WOCC 2017 - Newark, United States
持續時間: 7 四月 20178 四月 2017

出版系列

名字2017 26th Wireless and Optical Communication Conference, WOCC 2017

Conference

Conference26th Wireless and Optical Communication Conference, WOCC 2017
國家United States
城市Newark
期間7/04/178/04/17

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    Jeng, P., & Wang, L-C. (2017). Stream data analysis of body sensors for sleep posture monitoring: An automatic labelling approach. 於 2017 26th Wireless and Optical Communication Conference, WOCC 2017 [7928969] (2017 26th Wireless and Optical Communication Conference, WOCC 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WOCC.2017.7928969