权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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

纵向传感器数据的众包注释可增强行为健康的数据驱动精准医学

基本信息

批准号：
9078547
负责人：
STEPHEN S INTILLE
金额：
$ 29.94万
依托单位：
NORTHEASTERN UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-30 至 2018-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9078547
关键词：
Accelerometer Algorithms American Behavior Behavioral Behavioral Sciences Big Data Car Phone Classification Cohort Studies Communities Complex Computers Crowding Data Data Collection Data Quality Data Science Data Set Devices Environmental Exposure Environmental Risk Factor Evaluation Fostering Funding Future Genomics Goals Health Housing Human Imagery Individual Intervention Intervention Studies Interview Label Lead Light Location Machine Learning Medicine Metadata Methods Modeling Monitor Motion National Health and Nutrition Examination Survey Outcome Output Participant Pathway Analysis Pattern Performance Persons Physical activity Play Population Precision Medicine Initiative Process Proteomics Research Research Personnel Risk Behaviors Sampling Science Scientist Sleep Source Code Stream System Techniques Testing Time Training United States National Institutes of Health Women&apos s Health Work base behavioral health biobank citizen science crowdsourcing design fitness genetic makeup handheld mobile device improved innovation insight instrument interest learning network meetings metabolomics mobile application mobile computing novel open source precision medicine prototype public health relevance research study sedentary lifestyle sensor surveillance study temporal measurement tool ubiquitous computing usability

项目摘要

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.