Predicting Depression and Anxiety Mood by Wrist-Worn Sleep Sensor

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728147161
DOIs
Publication statusPublished - Mar 2020
Event2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020 - Austin, United States
Duration: Mar 23 2020Mar 27 2020

Publication series

Name2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020

Conference

Conference2020 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2020
Country/TerritoryUnited States
CityAustin
Period3/23/203/27/20

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Human-Computer Interaction
  • Safety, Risk, Reliability and Quality
  • Instrumentation

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