Transfer Learning in Body Sensor Networks Using Ensembles of Randomised Trees

In this work we investigate the process of transferring the activity recognition models of the nodes of a Body Sensor Network and we proposed a methodology that supports and makes the transferring possible. The methodology, based on a collaborative training strategy, makes use of classifier ensembles of randomised trees that allow to generate activity recognition models able to be successfully transferred through the nodes of the network. Experimental results evaluated on 17 subjects with a network of 5 wearable nodes with 5 everyday life activities show that the recognition models can be transferred to a new untrained node replacing a node previously present in the network without a significant loss in the recognition performance. Moreover, the models achieve good recognition performance in nodes located in previously unknown positions.