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

 描述(由申请者提供)：从手机和其他可穿戴传感器被动收集的纵向传感器数据将改变行为科学，允许研究人员使用“大数据”，但在个人层面上，了解行为和相关环境暴露如何影响健康结果。计算机将分析个人级别的数据流，以便在研究和干预中实现前所未有的个人级别的精度。这种类型的精确医学使科学和医学能够针对特定个人的基因构成、过去和现在的情况以及行为健康暴露。手机、智能手表和普通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一个基于移动和在线众源的系统的可行性和可用性，该系统用于清理和注释从运动传感器、移动电话和其他移动设备收集的行为数据。我们的目标是展示玩手机和网络游戏的个人--“人群”--如何集体地、以负担得起的方式逐步清理并将重要的元数据添加到被动从个人收集的原始传感器数据中，类似于人口规模监测研究(例如，国家健康和营养调查(NHANES)和英国生物库(UK Biobank))以及白宫精密医学倡议(Precision Medicine Initiative)等研究计划中的数据。因此，玩游戏的人群将极大地提高为各种科学研究收集的数据集的效用。我们将在用于研究身体活动、久坐行为和睡眠的运动监测器收集的数据集上验证我们的原型系统，但我们将演示如何将系统扩展到使用移动设备收集的日益丰富的数据集上，这些数据集不仅包括运动数据，还包括位置、光线、音频和人与人之间的距离的传感器数据。然后，我们将完善该系统，培养一个对公民科学感兴趣的大众游戏玩家社区，并将源代码作为开源项目发布给该系统，以便其他研究人员可以将该技术应用于他们自己的工作。

 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.