Probabilistic decision level fusion for real-time correlation of ambient and wearable sensors

Fusing data from ambient and wearable sensors when performing in-home healthcare monitoring allows for high accuracy activity inference due to the complementary nature of sensing modalities. Where residences may house multiple occupants, we must automatically identify related data streams before fusion may occur, a process known as sensor correlation. In this paper a multi-objective variant of the Bayesian Framework for Feature Selection (BFFS) is used to construct small inter-sensor redundant feature sets which train efficient per-sensor activity classifiers. Probabilistic decision level fusion is then used to deal with noisy and erroneous sensor data and perform real-time correlation. The potential value of the proposed algorithm for pervasive sensing is demonstrated with both simulated and experimental data.