Prediction of sleep efficiency from big physical exercise data
Liu, Xiaoli; Tamminen, Satu; Korhonen, Topi; Röning, Juha; Riekki, Jukka (2019-09-30)
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
© 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.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe2020042822773
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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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