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Summary: KNOWME: An Energy-Efficient, Multimodal Body
Area Network for Physical Activity Monitoring
GAUTAM THATTE, MING LI, SANGWON LEE, ADAR EMKEN, SHRIKANTH
NARAYANAN, URBASHI MITRA, DONNA SPRUIJT-METZ, MURALI ANNAVARAM
University of Southern California, Los Angeles
Submitted to WHS'09 Special Issue
The use of biometric sensors for monitoring an individual's health and related behaviors, continu-
ously and in real time, promises to revolutionize health care in the near future. In an effort to better
understand the complex interplay between one's medical condition and social, environmental and
metabolic parameters, this paper presents the KNOWME platform, which is a complete, end-to-
end, body area sensing system that integrates off-the-shelf biometric sensors with a Nokia N95
mobile phone to continuously monitor the metabolic signals of a subject. With a current focus on
pediatric obesity, KNOWME employs metabolic signals to monitor and evaluate physical activity.
KNOWME development and in-lab deployment studies have revealed three major challenges: (1)
the need for robustness to highly varying operating environments due to subject-induced variabil-
ity such as mobility or sensor placement, (2) balancing the tension between achieving high fidelity
data collection and minimizing network energy consumption, and (3) accurate physical activity
detection using a modest number of sensors. The KNOWME platform described herein directly
addresses these three challenges. Design robustness is achieved by creating a three-tiered sensor
data collection architecture. The system architecture is designed to provide robust continuous
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