title: Energy-efficient multihypothesis activity-detection for health-monitoring applications.
abstract: Multi-hypothesis activity-detection using a wireless 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 complexity, facilitating real-time implementation.
authors: Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA 90089, USA.
authors: Gautam
authors: G
authors: Thatte
authors: Ming
authors: M
authors: Li
authors: Adar
authors: A
authors: Emken
authors: Urbashi
authors: U
authors: Mitra
authors: Shri
authors: S
authors: Narayanan
authors: Murali
authors: M
authors: Annavaram
authors: Donna
authors: D
authors: Spruijt-Metz
mesh_terms: D000465:Algorithms; D003246:Conservation of Energy Resources / Q000379:methods; D004562:Electrocardiography / Q000379:methods; D008954:Models, Biological; D008991:Monitoring, Physiologic / Q000191:economics / Q000379:methods; D012815:Signal Processing, Computer-Assisted; D013686:Telemetry