Behavior-Oriented Data Resource Management in Medical Sensing Systems
Venue
ACM Transactions on Sensor Networks (TOSN), vol. 9 (2013), 12:1-12:26
Publication Year
2013
Authors
Hyduke Noshadi, Foad Dabiri, Saro Meguerdichian, Miodrag Potkonjak, Majid Sarrafzadeh
BibTeX
Abstract
Wearable sensing systems have recently enabled a variety of medical monitoring and
diagnostic applications in wireless health. The need for multiple sensors and
constant monitoring leads these systems to be power hungry and expensive with short
operating lifetimes. We introduce a novel methodology that takes advantage of
contextual and semantic properties in human behavior to enable efficient design and
optimization of such systems from the data and information point of view. This, in
turn, directly influences the wireless communication and local processing power
consumption. We exploit intrinsic space and temporal correlations between sensor
data while considering both user and system contextual behavior. Our goal is to
select a small subset of sensors that accurately capture and/or predict all
possible signals of a fully instrumented wearable sensing system. Our approach
leverages novel modeling, partitioning, and behavioral optimization, which consists
of signal characterization, segmentation and time shifting, mutual signal
prediction, and a simultaneous minimization composed of subset sensor selection and
opportunistic sampling. We demonstrate the effectiveness of the technique on an
insole instrumented with 99 pressure sensors placed in each shoe, which cover the
bottom of the entire foot, resulting in energy reduction of 72% to 97% for error
rates of 5% to 17.5%.
