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FieldStream (NetSE: Large: Collaborative Research: FieldStream: Network Data Services for Exposure Biology Studies in Natural Environments) is a collaborative project funded by the National Science Foundation and involving researchers from Carnegie Mellon University, Georgia Institute of Technology, University of California at Los Angeles, University of Massachusetts at Amherst, and University of Memphis. The project 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. 

In this project, which started in September 2009, a multidisciplinary team of researchers spanning various computing disciplines and behavioral sciences is 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. To help validate the assumptions, establish the feasibility of developed solutions, and to uncover new requirements, FieldStream technology will be incorporated in studies being conducted by the NIH sponsored AutoSense effort at Memphis and the NSF sponsored Urban Sensing effort at UCLA. 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.

This material is based upon work supported by the NSF under award # 0910592091070609107540910878 and 0910900. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF.