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: 0910754
• 负责人: Anind Dey
• 金额: $ 36万
• 依托单位: Carnegie-Mellon University
• 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=0910754&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.

该奖项是根据2009年《美国复苏与再投资法》（公法111-5）资助的。从自然环境中获得人类受试者的生理/行为数据对于进行生态有效的社会和行为研究至关重要。虽然当今有几个车身区域无线传感器网络（BAWSN）系统用于生理数据收集，但它们的使用仅限于受控设置（实验室，驾驶/飞行方案等）；大量的噪声，运动伪像以及其他无法控制的混杂因素的存在是经常引用的原因是不使用自然环境的生理测量。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）严格的数据质量而不感知冗余，（2）个性化，以说明人物在生理测量中的差异之间的较大范围，以及（3）实时推理以允许主题确认和及时干预。智力优点：在这个项目中，跨越各种计算学科和行为科学的多学科研究人员正在开发一个名为Fieldstream的通用框架，这将使BAWSN系统有可能长期无关紧要的无关紧要的，持续和可靠的生理/行为数据收集，这些自然环境来自自然界，该自然环境可用于进行基于人群的科学研究。 Fieldstream正在将两个现实生活项目纳入吗？ NIH赞助了孟菲斯和NSF赞助的UCLA的Urban Sensing AutoSense，以帮助验证假设，确定开发解决方案的可行性并发现新的要求。更广泛的影响：通过使现场获得科学有效的客观数据成为可能，Fieldstream有望帮助解决一些对人类社会至关重要的行为问题，而这些问题对于人类社会至关重要，而这些问题由于缺乏此类数据而无法得到答复。