Publications

Data and Expert Models for Sleep Timing and Chronotype Estimation from Smartphone Context Data and Simulations

Publication Type Journal Article
Authors Florian Wahl, Oliver Amft
Title Data and Expert Models for Sleep Timing and Chronotype Estimation from Smartphone Context Data and Simulations
Abstract We present a sleep timing estimation approach that combines data-driven estimators with an expert model and uses smartphone context data. Our data-driven methodology comprises a classifier trained on features from smartphone sensors. Another classifier uses time as input. Expert knowledge is incorporated via the human circadian and homeostatic two process model. We investigate the two process model as output filter on classifier results and as fusion method to combine sensor and time classifiers. We analyse sleep timing estimation performance, in data from a two-week free-living study of 13 participants and sensor data simulations of arbitrary sleep schedules, amounting to 98280 nights. Five intuitive sleep parameters were derived to control the simulation. Moreover, we investigate model personalisation, by retraining classifiers based on participant feedback. The joint data and expert model yields an average relative estimation error of -2±62 min for sleep onset and -5±70 min for wake (absolute errors 40±48 min and 42±57 min, mean median absolute deviation 22 min and 15 min), which significantly outperforms data-driven methods. Moreover, the data and expert models combination remains robust under varying sleep schedules. Personalising data models with user feedback from the last two days showed the largest performance gain of 57% for sleep onset and 59% for wake up. Our power-efficient smartphone app makes convenient everyday sleep monitoring finally realistic.
Publication Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.
Volume 2
Issue 3
Pages 139:1–139:28
Date September 2018
DOI 10.1145/3264949
ISSN 2474-9567
URL Publisher's website
Library Catalog ACM Digital Library
Full Text PDF
Friedrich-Alexander-Universität Erlangen-Nürnberg