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
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Publish Date: | 2020-04-28 |
Description: |
AbstractPhysical 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. see all
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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. |