Obesity is currently affecting very large portions of the global population. Effective prevention and treatment starts at the early age and requires objective knowledge of population-level behavior on the region/neighborhood scale. To this end, we present a system for extracting and collecting behavioral information on the individual-level objectively and automatically. The behavioral information is related to physical activity, types of visited places, and transportation mode used between them. The system employs indicator-extraction algorithms from the literature which we evaluate on publicly available datasets. The system has been developed and integrated in the context of the EU-funded BigO project that aims at preventing obesity in young populations.
@article{arxiv.2005.04928,
title = {Collecting big behavioral data for measuring behavior against obesity},
author = {Vasileios Papapanagiotou and Ioannis Sarafis and Christos Diou and Ioannis Ioakimidis and Evangelia Charmandari and Anastasios Delopoulos},
journal= {arXiv preprint arXiv:2005.04928},
year = {2021}
}
Comments
Accepted version to be published in 2020, 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Montreal, Canada