Optimal time-resource allocation for activity-detection via multimodal sensing
The optimal allocation of measurements for activity-level detection in a wireless body area network (WBAN) for health-monitoring applications is considered. The WBAN with heterogeneous sensors is deployed in a simple star topology with the fusion center receiving a fixed number of measurements from the sensors; the number of measurements allocated to each sensor is optimized to minimize the probability of detection error at the fusion center. An analysis of the two-sensor case with binary hypotheses is presented. Since the number of measurements is an integer, an exhaustive search (grid search) is traditionally employed to determine the optimal allocation of measurements. However, such a search is computationally expensive. To this end, an alternate continuous-valued vector optimization is derived which yields approximately optimal allocations which can be found with lower complexity. Numerical case studies based on experimental data for different key activity-states are presented. It is observed that the Kullback-Leibler (KL) distances between the distributions associated with the hypotheses dominate the optimal allocation of measurements. Copyright 2009 ICST.
