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BioMed Research International
Volume 2017, Article ID 4593956, 15 pages
Research Article

The Double Layer Methodology and the Validation of Eigenbehavior Techniques Applied to Lifestyle Modeling

Wearable Health Solutions, Holst Centre, High Tech Campus 31, 5656 AE Eindhoven, Netherlands

Correspondence should be addressed to Giuseppina Schiavone; moc.liamg@enovaihcs.ysuig

Received 28 June 2016; Revised 7 September 2016; Accepted 22 November 2016; Published 4 January 2017

Academic Editor: Rita Casadio

Copyright © 2017 Giuseppina Schiavone et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A novel methodology, the double layer methodology (DLM), for modeling an individual’s lifestyle and its relationships with health indicators is presented. The DLM is applied to model behavioral routines emerging from self-reports of daily diet and activities, annotated by 21 healthy subjects over 2 weeks. Unsupervised clustering on the first layer of the DLM separated our population into two groups. Using eigendecomposition techniques on the second layer of the DLM, we could find activity and diet routines, predict behaviors in a portion of the day (with an accuracy of 88% for diet and 66% for activity), determine between day and between individual similarities, and detect individual’s belonging to a group based on behavior (with an accuracy up to 64%). We found that clustering based on health indicators was mapped back into activity behaviors, but not into diet behaviors. In addition, we showed the limitations of eigendecomposition for lifestyle applications, in particular when applied to noisy and sparse behavioral data such as dietary information. Finally, we proposed the use of the DLM for supporting adaptive and personalized recommender systems for stimulating behavior change.