The Science of Sweet Dreams

Predicting Sleep Efficiency from Wearable Device Data

Aarti Sathyanarayana, Jaideep Srivastava, Luis Fernandez

Research output: Contribution to specialist publicationArticle

7 Citations (Scopus)

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
Pages30-38
Number of pages9
Volume50
No.3
Specialist publicationComputer
DOIs
Publication statusPublished - 1 Mar 2017

Fingerprint

Health
Medical problems
Computer science
Students
Sleep

Keywords

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

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

The Science of Sweet Dreams : Predicting Sleep Efficiency from Wearable Device Data. / Sathyanarayana, Aarti; Srivastava, Jaideep; Fernandez, Luis.

In: Computer, Vol. 50, No. 3, 01.03.2017, p. 30-38.

Research output: Contribution to specialist publicationArticle

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