Crowd-Sourced Annotation of Longitudinal Sensor Data to Enhance Data-Driven Precision Medicine for Behavioral Health

 描述（由申请人提供）：从移动的手机和其他可穿戴传感器被动收集的纵向传感器数据将通过允许研究人员使用“大数据”来改变行为科学，但在人的层面上，了解行为和相关的环境暴露如何影响健康结果。计算机将分析个人层面的数据流，以实现前所未有的个人层面的研究和干预精度。这种类型的精准医学使科学和医学能够针对特定个体的基因组成，过去和现在的情况以及行为健康暴露。移动的手机、智能手表和常见的健身设备已经能够生成丰富的行为数据，但开发使用最新机器学习算法解释原始数据的算法需要实用的策略来注释大型数据集。我们建议开发和测试的可行性和可用性的移动的和在线基于众包的系统，用于清洁和注释从运动传感器，移动的手机，和其他移动的设备收集的行为数据。我们的目标是演示玩移动的和在线游戏的个人-“人群”-如何集体地、负担得起地、增量地清理并向从个人被动收集的原始传感器数据添加重要的元数据，类似于从人群规模的监测研究中收集的数据（例如，国家健康和营养检查调查（NHANES）和英国生物银行）和那些计划用于研究，如白宫的精准医学倡议。因此，玩游戏的人群将大大提高为各种科学研究收集的数据集的效用。我们将在从用于研究身体活动、久坐行为和睡眠的运动监视器收集的数据集上验证我们的原型系统，但我们将演示如何扩展该系统以用于越来越丰富的数据集，这些数据集是通过移动的设备收集的，不仅包括运动数据，还包括位置、光线、音频和人与人之间的接近度等传感器数据。然后，我们将完善系统，培养一个对公民科学感兴趣的群体游戏玩家社区，并将源代码作为开源项目发布到系统中，以便其他研究人员可以将该技术应用于自己的工作。

 DESCRIPTION (provided by applicant): Longitudinal sensor data collected passively from mobile phones and other wearable sensors will transform behavioral science by allowing researchers to use "big data," but at the person-level, to understand how behavior and related environmental exposures impact health outcomes. Computers will analyze individual-level data streams to permit unprecedented, individual-level precision in research and intervention. This type of precision medicine enables targeting of science and medicine to a particular individual's genetic makeup, past and current situation, and behavioral health exposures. Mobile phones, smartwatches, and common fitness devices are already capable of generating rich data on behavior, but developing algorithms to interpret that raw data using the latest machine learning algorithms requires practical strategies to annotate large datasets. We propose to develop and test the feasibility and usability of a mobile and online crowdsource-based system for cleaning and annotating behavioral data collected from motion sensors, mobile phones, and other mobile devices. Our goal is to demonstrate how individuals playing mobile and online games - the "crowd" - can collectively, affordably, and incrementally clean and add important metadata to raw sensor data that has been passively collected from individuals, similar to that from population-scale surveillance studies (e.g., the National Health and Nutrition Examination Survey (NHANES) and UK Biobank) and those planned for studies such as the White House's Precision Medicine Initiative. The game-playing crowd will thereby dramatically improve the utility of the datasets collected for a variety of scientific studies. We will validate our prototye system on datasets collected from motion monitors used to study physical activity, sedentary behavior, and sleep, but we will demonstrate how the system could be extended for use on the increasingly rich datasets that are being collected with mobile devices and that include not only motion data, but also sensor data on location, light, audio, and person-to-person proximity. We will then refine the system, foster a community of crowd game players interested in citizen science, and release the source code to the system as an open source project so that other researchers can adapt the technique for their own work.