Transfer Learning in Body Sensor Networks Using Ensembles of Randomised Trees

In this work we investigate the process of transferringthe activity recognition models of the nodes of a BodySensor Network and we proposed a methodology that supportsand makes the transferring possible. The methodology, based on acollaborative training strategy, makes use of classifier ensemblesof randomised trees that allow to generate activity recognitionmodels able to be successfully transferred through the nodes ofthe network. Experimental results evaluated on 17 subjects witha network of 5 wearable nodes with 5 everyday life activities showthat the recognition models can be transferred to a new untrainednode replacing a node previously present in the network withouta significant loss in the recognition performance. Moreover, themodels achieve good recognition performance in nodes located inpreviously unknown positions.