University of Oulu

Xiaoli Liu, Satu Tamminen, Topi Korhonen, Juha Röning, and Jukka Riekki. 2019. Prediction of sleep efficiency from big physical exercise data. In Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers (UbiComp/ISWC ’19 Adjunct). Association for Computing Machinery, New York, NY, USA, 1186–1189. DOI:https://doi.org/10.1145/3341162.3347078

Prediction of sleep efficiency from big physical exercise data

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Author: Liu, Xiaoli1; Tamminen, Satu1; Korhonen, Topi2;
Organizations: 1Biomimetics and Intelligent Systems Group, University of Oulu Oulu, Finland
2Polar Electro Oy Kempele, Finland
3Center for Ubiquitous Computing, University of Oulu Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.5 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2020042822773
Language: English
Published: Association for Computing Machinery, 2019
Publish Date: 2020-04-28
Description:

Abstract

Physical exercise can improve sleep quality. However, how to perform physical exercise to achieve the best possible improvements is not clear. In this article, we build predictive models based on volume real data collected from wearable devices to predict the sleep efficiency related to users’ daily exercise information. As far as we know, this is the first study to investigate insights of prediction of sleep efficiency from volume physical exercise data collected from real world.

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ISBN Print: 978-1-4503-6869-8
Pages: 1186 - 1189
DOI: 10.1145/3341162.3347078
OADOI: https://oadoi.org/10.1145/3341162.3347078
Host publication: 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2019 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2019
Conference: ACM International Joint Conference on Pervasive and Ubiquitous Computing
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
Subjects:
Funding: We thank Polar Electro Oy for providing the data and platform for experimentation. The first author also thank to Finnish Cultural Foundation for supporting this research.
Copyright information: © 2019 Association for Computing Machinery. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2019 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2019, https://doi.org/10.1145/3341162.3347078.