NapWell: An EOG-based Sleep Assistant Exploring the Effects of Virtual Reality on Sleep Onset

Yun Suen Pai, Marsel L. Bait, Juyoung Lee, Jingjing Xu, Roshan L. Peiris, Woontack Woo, Mark Billinghurst, Kai Kunze

Research output: Contribution to journalArticlepeer-review

Abstract

We present NapWell, a Sleep Assistant using virtual reality (VR) to decrease sleep onset latency by providing a realistic imagery distraction prior to sleep onset. Our proposed prototype was built using commercial hardware and with relatively low cost, making it replicable for future works as well as paving the way for more low cost EOG-VR devices for sleep assistance. We conducted a user study (n= 20) by comparing different sleep conditions; no devices, sleeping mask, VR environment of the study room and preferred VR environment by the participant. During this period, we recorded the electrooculography (EOG) signal and sleep onset time using a finger tapping task (FTT). We found that VR was able to significantly decrease sleep onset latency. We also developed a machine learning model based on EOG signals that can predict sleep onset with a cross-validated accuracy of 70.03%. The presented study demonstrates the feasibility of VR to be used as a tool to decrease sleep onset latency, as well as the use of embedded EOG sensors with VR for automatic sleep detection.

Original languageEnglish
JournalVirtual Reality
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Electrooculography
  • Sleep onset
  • Virtual reality

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design

Fingerprint

Dive into the research topics of 'NapWell: An EOG-based Sleep Assistant Exploring the Effects of Virtual Reality on Sleep Onset'. Together they form a unique fingerprint.

Cite this