客观测量行为与环境以支持儿童肥胖大规模研究的系统
A system for objectively measuring behavior and the environment to support large-scale studies on childhood obesity
AI总结：
本文提出一个集成系统，利用智能手机和智能手表被动收集并提取行为与环境指标，以支持儿童肥胖的大规模研究，算法在多项指标上表现良好，对公共卫生政策制定具重要价值。
摘要：
IoT技术的进步结合新算法，使得收集和处理高速率多源数据流成为可能，这些数据流能细粒度地量化人类行为，并为个体行为以及行为与环境之间的相互作用提供更深入的见解。本文提出一个集成系统，用于收集和提取多种行为与环境指标，旨在改善应对肥胖的公共卫生政策。数据收集采用基于智能手机和智能手表应用的被动方法，只需与用户进行最少的交互。我们的目标是详细阐述设计原则、实现过程以及集成算法的评估，特别是考虑到我们所面临的挑战，具体包括（a）在单一统一系统下集成多种技术、算法和组件，以及（b）大规模（大数据）要求。我们还展示了算法在数据集（多数为公开数据集）上的评估结果，例如计步时的绝对误差为8-9步，检测访问位置的F1分数为0.86，总睡眠时间的误差小于12分钟。最后，我们简要介绍了使用我们系统已实现的研究，从而证明其对公共当局和个体研究人员的潜在价值。
英文摘要：
Advances in IoT technologies combined with new algorithms have enabled the collection and processing of high-rate multi-source data streams that quantify human behavior in a fine-grained level and can lead to deeper insights on individual behaviors as well as on the interplay between behaviors and the environment. In this paper, we present an integrated system that collects and extracts multiple behavioral and environmental indicators, aiming at improving public health policies for tackling obesity. Data collection takes place using passive methods based on smartphone and smartwatch applications that require minimal interaction with the user. Our goal is to present a detailed account of the design principles, the implementation processes, and the evaluation of integrated algorithms, especially given the challenges we faced, in particular (a) integrating multiple technologies, algorithms, and components under a single, unified system, and (b) large scale (big data) requirements. We also present evaluation results of the algorithms on datasets (public for most cases) such as an absolute error of 8-9 steps when counting steps, 0.86 F1-score for detecting visited locations, and an error of less than 12 mins for gross sleep time. Finally, we also briefly present studies that have been materialized using our system, thus demonstrating its potential value to public authorities and individual researchers.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
