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Energy-Efficient Multihypothesis Activity-Detection for Health-Monitoring Applications
 

Summary: Energy-Efficient Multihypothesis Activity-Detection
for Health-Monitoring Applications
Gautam Thatte, Ming Li, Adar Emken, Urbashi Mitra,
Shri Narayanan, Murali Annavaram and Donna Spruijt-Metz
Abstract-- Multi-hypothesis activity-detection using a wire-
less body area network is considered. A fusion center receives
samples of biometric signals from heterogeneous sensors. Due
to the different discrimination capabilities of each sensor, an
optimized allocation of samples per sensor results in lower
energy consumption. Optimal sample allocation is determined
by minimizing the probability of misclassification given the
current activity state of the user. For a particular scenario,
optimal allocation can achieve the same accuracy (97%) as
equal allocation across sensors with an energy savings of
26%. As the number of samples is an integer, further energy
reduction is achieved by developing an approximation to the
probability of misclassification which allows for a continuous-
valued vector optimization. This alternate optimization yields
approximately optimal allocations with significantly lower com-
plexity, facilitating real-time implementation.

  

Source: Annavaram, Murali - Department of Electrical Engineering, University of Southern California

 

Collections: Engineering; Computer Technologies and Information Sciences