Papapanagiotou Vasileios, Sarafis Ioannis, Diou Christos, Ioakimidis Ioannis, Charmandari Evangelia, Delopoulos Anastasios
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:5296-5299. doi: 10.1109/EMBC44109.2020.9175313.
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.
肥胖目前正影响着全球很大一部分人口。有效的预防和治疗要从早年开始，并且需要在区域/社区层面客观了解人群行为。为此，我们提出了一个系统，用于客观、自动地提取和收集个体层面的行为信息。这些行为信息与身体活动、到访场所的类型以及其间使用的交通方式有关。该系统采用了文献中的指标提取算法，并在公开可用数据集上进行了评估。该系统是在欧盟资助的旨在预防年轻人群肥胖的BigO项目背景下开发和集成的。
