Estimation of sleep onset and awaking time using a deep neural network with physiological data during sleep

• Tsuchiya Minami

  Graduate School of Science and Engineering, Yamagata University

• Tanaka Atsushi

  Graduate School of Science and Engineering, Yamagata University

• Yasuda Muneki

  Graduate School of Science and Engineering, Yamagata University

• Harada Tomochika

  Graduate School of Science and Engineering, Yamagata University

• Cho Seung-Il

  Graduate School of Science and Engineering, Yamagata University

• Yokoyama Michio

  Graduate School of Science and Engineering, Yamagata University

抄録

A deep Neural Network (DNN) is used to estimate the sleep onset and awaking time of a subject with physiological data during sleep, in order to control the sleeping environment based on sleep phase. The results of the estimation from 40 minutes before the actual sleeping time show approximately 2.8 minutes mean error. Regarding awaking time, the results of the estimation from 120 minutes before show approximately 9.9 minutes mean error. Furthermore, the results of estimation in case of the range from 60 to 20 minutes before the actual awakening time show approximately 7.5 minutes mean error. The proposed DNN estimation is found to be effective for control of a comfortable awaking environment.

収録刊行物

• Nonlinear Theory and Its Applications, IEICE

  Nonlinear Theory and Its Applications, IEICE 10 (4), 366-372, 2019

  一般社団法人 電子情報通信学会

キーワード

• big data analysis
• deep neural network
• sleep quality
• environmental and vital data sensing

詳細情報

• CRID: 1390845702294343296
• NII論文ID: 130007722645
• DOI: 10.1587/nolta.10.366
• ISSN: 21854106
• Web Site: https://www.jstage.jst.go.jp/article/nolta/10/4/10_366/_pdf
• 本文言語コード: en
• データソース種別:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• 抄録ライセンスフラグ: 使用不可