The Science of Sweet Dreams: Predicting Sleep Efficiency from Wearable Device Data

Aarti Sathyanarayana, Jaideep Srivastava, Luis Fernandez-Luque

Research output: Contribution to specialist publicationArticle

10 Scopus citations

Abstract

Lack of sleep can Erode mental and physical well-being, often exacerbating health problems such as obesity. Wearable devices that capture and analyze sleep quality through predictive methodologies can help patients and medical practitioners make behavioral health decisions that can lead to better sleep and improved health. In the web extra at https://youtu.be/-zL-t4gk210, guest editor Katarzyna Wac interviews lead author Aarti Sathyanarayana, a PhD student in the University of Minnesota's Department of Computer Science.

Original languageEnglish (US)
Pages30-38
Number of pages9
Volume50
No3
Specialist publicationComputer
DOIs
StatePublished - Mar 2017

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Keywords

  • QoL technologies
  • big data
  • data analysis
  • health monitoring
  • health tracking
  • healthcare
  • mobile
  • quality-of-life technologies
  • sleep
  • sleep science
  • wearable devices

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