Prediction of sleep efficiency from big physical exercise data

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.

Authors:
Liu Xiaoli, Tamminen Satu, Korhonen Topi, Röning Juha, Riekki Jukka

Publication type:
A4 Article in conference proceedings

Place of publication:
2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2019 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2019

Keywords:
Physical exercise, Predictive models, Sleep quality

Published:

Full citation:
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

DOI:
https://doi.org/10.1145/3341162.3347078

Read the publication here:
http://urn.fi/urn:nbn:fi-fe2020042822773