Personalization of Energy Expenditure Estimation in Free Living Using Topic Models

Publication Type Journal Article
Authors Marco Altini, Pierluigi Casale, Julien Penders, Oliver Amft
Title Personalization of Energy Expenditure Estimation in Free Living Using Topic Models
Abstract We introduce an approach to personalize energy expenditure (EE) estimates in free living. First we use Topic Models (TM) to discover activity composites from recognized activity primitives and stay regions in daily living data. Subsequently, we determine activity composites that are relevant to contextualize heart rate (HR). Activity composites were ranked and analyzed to optimize the correlation to HR normalization parameters. Finally, individual-specific HR normalization parameters were used to normalize HR. Normalized HR was then included in activity-specific regression models to estimate EE. Our HR normalization minimizes the effect of individual fitness differences from entering in EE regression models. By estimating HR normalization parameters in free living, our approach avoids dedicated individual calibration or laboratory tests. In a combined free-living and laboratory study dataset, including $34$ healthy volunteers, we show that HR normalization in 14-day free living data improves accuracy compared to no normalization and normalization based on activity primitives only ($29.4$% and $19.8$% error reduction against lab reference). Based on acceleration and HR, both recorded from a necklace, and GPS acquired from a smartphone, EE estimation error was reduced by $10.7$% in a leave-one-participant-out analysis.
Publication IEEE Journal of Biomedical and Health Informatics
Volume 19
Issue 5
Pages 1577–1586
Date March 2015
DOI 10.1109/JBHI.2015.2418256
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Friedrich-Alexander-Universität Erlangen-Nürnberg