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Body Sensor Networks: An Application-Centric Approach 

Date: Wednesday September 12, 2012

Speaker: John Lach, PhD, University of Virginia - Click Here for the speaker's bio.

Subject: "Body Sensor Networks: An Application-Centric Approach"

View the presentation - 2.45 MB PDF


Wireless body sensor networks (BSNs) have emerged in recent years to address a significant and persistent challenge in healthcare and medical research – the continuous, non-invasive, inexpensive collection of high-quality patient data. Longer battery lifetimes, smaller form factors, and higher manufacturing volumes have contributed to making BSN data collection more continuous, non-invasive, and inexpensive, respectively, but progress towards the demonstration of high-quality data is lacking. Quality is ultimately an application-specific measure, requiring BSNs to be deployed and evaluated in real application settings, collecting data on real patients and tracking the impact of that data. Only then can the true value of BSNs be evaluated – ultimately using improved patient outcomes and reduced healthcare costs as the metrics for such evaluation – and can future BSN research be informed.

This presentation will discuss ongoing application-centric BSN research at the UVA Center for Wireless Health and partner institutions. The overarching methodology includes an ongoing cycle of BSN development enabling application deployment informing advanced research leading to further development, all with the goal of providing high-quality data as measured by the target applications. The primary development system to be presented is TEMPO – a custom inertial BSN platform that provides six degrees-of-freedom wireless motion capture in a wristwatch form factor. Applications that will be discussed – all of which include human subject studies with TEMPO deployed on the target patient population – include fall risk assessment and fall prevention, agitation assessment in dementia patients, and orthopedic assessment for children with cerebral palsy. Advanced research leveraging this application experience includes strategies for dynamic power management and signal processing methods to extract medically relevant information from raw sensor data.

Biographical Information:

John Lach received the B.S. (1996) degree in Science, Technology, and Society from Stanford University and the M.S. (1998) and Ph.D. (2000) degrees in Electrical Engineering from UCLA. Since 2000, he has been a faculty member in the Charles L. Brown Department of Electrical and Computer Engineering at the University of Virginia. He is a Senior Member of the IEEE and is a former Associate Editor for the IEEE Transactions on Computers and the IEEE Transactions on Computer Aided Design of Integrated Circuits and Systems. He is a Co-Founder and Steering Committee member for the Wireless Health conference series and is a Co-Founder and Co-Director of the UVA Center for Wireless Health. He has been the PI or co-PI on 30 grants and has published over 100 refereed papers, including three Best Paper Awards. His primary research interests include wireless health, body sensor networks, embedded systems, and digital system design methodologies.

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