KNOWME: An Energy-Efficient Multimodal Body Area Network for Physical Activity Monitoring

  • Authors:

  • Gautam Thatte;Ming Li;Sangwon Lee;Adar Emken;Shrikanth Narayanan;Urbashi Mitra;Donna Spruijt-Metz;Murali Annavaram

  • Affiliations:

  • University of Southern California, Los Angeles;University of Southern California, Los Angeles;University of Southern California, Los Angeles;University of Southern California, Los Angeles;University of Southern California, Los Angeles;University of Southern California, Los Angeles;University of Southern California, Los Angeles;University of Southern California, Los Angeles

  • Venue:
  • ACM Transactions on Embedded Computing Systems (TECS) - Special Section on CAPA'09, Special Section on WHS'09, and Special Section VCPSS' 09
  • Year:
  • 2012

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Abstract

The use of biometric sensors for monitoring an individual’s health and related behaviors, continuously and in real time, promises to revolutionize healthcare 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 article presents the KNOWME platform, 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 variability, 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, multichannel data collection and scales without compromising normal mobile device operation. Novel physical activity detection methods which exploit new representations of sensor signals provide accurate and efficient physical activity detection. The physical activity detection method employs personalized training phases and accounts for intersession variability. Finally, exploiting the features of the hardware implementation, a low-complexity sensor sampling algorithm is developed, resulting in significant energy savings without loss of performance.