Mining hidden correlations between sleep and lifestyle factors from quantified-self data-Reference-Cited by-同舟云学术

Mining hidden correlations between sleep and lifestyle factors from quantified-self data

Published:2016-09-12 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct
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Author:

Liang Zilu¹,Martell Mario Alberto Chapa¹,Nishimura Takuichi²

Affiliation:

1. The University of Tokyo, Japan

2. National Institute of Advanced Industrial, Science and Technology, Japan

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/2968219.2968319

Reference9 articles.

1. Frank Bentley Konrad Tollmar Peter Stephenson etal 2013. Health Mashups: Presenting Statistical Patterns between Wellbeing Data and Context in Natural Language to Promote Behavior Change. ACM Transactions on Computer-Human Interaction (TOCHI) 20:5 30. 10.1145/2503823 Frank Bentley Konrad Tollmar Peter Stephenson et al. 2013. Health Mashups: Presenting Statistical Patterns between Wellbeing Data and Context in Natural Language to Promote Behavior Change. ACM Transactions on Computer-Human Interaction (TOCHI) 20:5 30. 10.1145/2503823

2. SleepTight

3. Zilu Liang Bernd Ploderer Wanyu Liu etal (In press) SleepExplorer: a visualization tool to make sense of correlations between personal sleep data and contextual factors. Personal and Ubiquitous Computing. Zilu Liang Bernd Ploderer Wanyu Liu et al. (In press) SleepExplorer: a visualization tool to make sense of correlations between personal sleep data and contextual factors. Personal and Ubiquitous Computing.

4. Mining gene expression databases for association rules

5. Normal Human Sleep: An Overview

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