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

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。从人类受试者的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。虽然目前存在几种用于生理数据收集的身体区域无线传感器网络（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）；明显的噪声、运动伪影和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常见原因。为了从自然环境中提供科学有效的数据，BAWSN系统必须满足以下几个独特的要求：(1)严格的数据质量，无传感冗余；(2)个性化，以考虑生理测量中人与人之间的巨大差异；(3)实时推理，以允许受试者确认和及时干预。智力优势：在这个项目中，一个由不同计算学科和行为科学的多学科研究人员组成的团队正在开发一个名为FieldStream的通用框架，该框架将使BAWSN系统能够长期无人值守地收集客观、连续和可靠的生理/行为数据，这些数据来自自然环境，可用于开展基于人口的科学研究。FieldStream正在并入两个现实生活中的项目？美国国立卫生研究院资助了孟菲斯的AutoSense，美国国家科学基金会资助了加州大学洛杉矶分校的Urban Sensing，以帮助验证假设，建立开发解决方案的可行性，并发现新的需求。更广泛的影响：通过使从现场获得科学有效的客观数据成为可能，FieldStream有望帮助解决几个对人类社会至关重要的行为问题，这些问题由于缺乏此类数据而仍未得到解答。

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.