Abstract

The optimal allocation of samples for physical activity detection in a wireless body area network for health-monitoring is considered. The number of biometric samples collected at the mobile device fusion center, from both device-internal and external Bluetooth heterogeneous sensors, is optimized to minimize the transmission power for a fixed number of samples, and to meet a performance requirement defined using the probability of misclassification between multiple hypotheses. A filter-based feature selection method determines an optimal feature set for classification, and a correlated Gaussian model is considered. Using experimental data from overweight adolescent subjects, it is found that allocating a greater proportion of samples to sensors which better discriminate between certain activity levels can result in either a lower probability of error or energy-savings ranging from 18% to 22%, in comparison to …

Date

2011

Authors

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

Journal

IEEE Transactions on Signal Processing

Volume

59

Issue

4

Pages

1843-1857

Publisher

IEEE