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

Aarti Sathyanarayana; Jaideep Srivastava; Luis Fernandez-Luque

doi:10.1109/MC.2017.91

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

Aarti Sathyanarayana, Jaideep Srivastava, Luis Fernandez-Luque

Computer Science and Engineering

Research output: Contribution to specialist publication › Article

27 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 language	English (US)
Pages	30-38
Number of pages	9
Volume	50
No	3
Specialist publication	Computer
DOIs	https://doi.org/10.1109/MC.2017.91
State	Published - Mar 2017

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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

Abstract

Bibliographical note

Keywords

UN SDGs

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