NetSE: Large: Collaborative Research: FieldStream: Network Data Services for Exposure Biology Studies in Natural Environments

NetSE：大型：协作研究：FieldStream：自然环境中暴露生物学研究的网络数据服务

• 批准号: 0910878
• 负责人: Santosh Kumar
• 金额: $ 79万
• 依托单位: University of Memphis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0910878&HistoricalAwards=false
• 关键词: NetSE; Large; Collaborative; Research; FieldStream

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). Obtaining physiological/behavioral data from human subjects in their natural environments is essential to conducting ecologically valid social and behavioral research. While several body area wireless sensor network (BAWSN) systems exist today for physiological data collection, their use has been restricted to controlled settings (laboratories, driving/flying scenarios, etc.); significant noise, motion artifacts, and existence of other uncontrollable confounding factors are the often cited reasons for not using physiological measurements from natural environments. In order to provide scientifically valid data from natural environments, a BAWSN system must meet several unique requirements (1) Stringent data quality without sensing redundancy, (2) Personalization to account for wide between person differences in physiological measurements, and (3) Real-time inferencing to allow for subject confirmation and timely intervention.Intellectual Merit: In this project, a multidisciplinary team of researchers spanning various computing disciplines and behavioral sciences are developing a general purpose framework called FieldStream that will make it possible for BAWSN systems to provide long term unattended collection of objective, continuous, and reliable physiological/behavioral data from natural environments that can be used for conducting population based scientific studies. FieldStream is being incorporated in two real-life projects ? NIH sponsored AutoSense at Memphis and NSF sponsored Urban Sensing at UCLA, to help validate the assumptions, establish the feasibility of developed solutions, and to uncover new requirements. Broader Impact: By making it possible to obtain scientifically valid objective data from the field, FieldStream promises to help solve several behavioral problems of critical importance to human society that have remained unanswered for lack of such data.

该奖项根据 2009 年美国复苏和再投资法案（公法 111-5）提供资金。从人类受试者在自然环境中获取生理/行为数据对于进行生态有效的社会和行为研究至关重要。虽然目前存在一些用于收集生理数据的体域无线传感器网络 (BAWSN) 系统，但它们的使用仅限于受控环境（实验室、驾驶/飞行场景等）；显着的噪声、运动伪影以及其他不可控混杂因素的存在是不使用自然环境生理测量的经常被引用的原因。为了提供来自自然环境的科学有效数据，BAWSN 系统必须满足几个独特的要求 (1) 严格的数据质量，无感知冗余，(2) 个性化，以考虑生理测量中人与人之间的广泛差异，以及 (3) 实时推理，以允许受试者确认和及时干预。 智力优点：在该项目中，跨越不同计算学科和行为科学的多学科研究人员团队正在开发一种 称为 FieldStream 的通用框架将使 BAWSN 系统能够长期无人值守地收集来自自然环境的客观、连续和可靠的生理/行为数据，这些数据可用于进行基于人口的科学研究。 FieldStream 正在被纳入两个现实项目中？ NIH 赞助了孟菲斯的 AutoSense，NSF 赞助了 UCLA 的 Urban Sensing，以帮助验证假设、确定已开发解决方案的可行性并发现新的需求。更广泛的影响：通过从现场获取科学有效的客观数据，FieldStream 有望帮助解决一些对人类社会至关重要的行为问题，而这些问题由于缺乏此类数据而尚未得到解答。