Transfer Learning in Body Sensor Networks Using Ensembles of Randomized Trees

We investigate the process of transferring the activity recognition models within the nodes of a body sensor network (BSN). In particular, we propose a methodology that supports and makes the transferring possible. Based on a collaborative training strategy, classifier ensembles of randomized trees are used to create activity recognition models that can successfully be transferred within the nodes of the network. The methodology has been applied in scenarios where a node present in the network is replaced by a new node located in the same position (replacement scenario) and relocated to a previously unknown position (relocation scenario). Experimental results show that the transferred recognition models achieve high-recognition performance in the replacement scenario and good-recognition performance are achieved in the relocation scenario. Results have been validated with multiple K-folds cross-validations in order to test the performance of the methodology when different amount of data are shared between nodes.