Predicting Depression and Anxiety Mood by Wrist-Worn Sleep Sensor

Shuichi Fukuda, Yuki Matsuda, Yuri Tani, Yutaka Arakawa, Keiichi Yasumoto

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

In recent years, researches on recognizing daily behavior and psychological/physiological states have been actively conducted to change the behavior of workers working in companies. In this paper, we analyzed Occupational Health questionnaire named DAMS for waking-up time and daily sleep data that are acquired from wearable devices in 2-3 weeks experiment of 60 office workers working in five general companies. By using a machine learning method, our binary Balanced Random Forest model predicts depression, positive, and anxiety moods in two levels, high and low. As a result of Leave One Person Out cross validation, it was confirmed that our model estimated with the F1 values of depression mood: 0.776, positive mood: 0.610, anxiety mood: 0.756. Moreover, we evaluated the variance of the three estimations among subjects by referencing the box chart. As a result, it was confirmed that there is variance in estimation accuracy for each subject.

本文言語英語
ホスト出版物のタイトル2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728147161
DOI
出版ステータス出版済み - 3 2020
イベント2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020 - Austin, 米国
継続期間: 3 23 20203 27 2020

出版物シリーズ

名前2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020

会議

会議2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020
国/地域米国
CityAustin
Period3/23/203/27/20

All Science Journal Classification (ASJC) codes

  • コンピュータ ネットワークおよび通信
  • コンピュータ サイエンスの応用
  • ハードウェアとアーキテクチャ
  • 人間とコンピュータの相互作用
  • 安全性、リスク、信頼性、品質管理
  • 器械工学

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