Detecting and Rectifying Anomalies in Body Sensor Networks

Activity recognition using on body sensors are prone to degradation due to changes on sensor readings. The changes can occur because of degradation or alteration in the behaviour of the sensor with respect to the others. In this paper we propose a method which detects anomalous nodes in the network and takes compensatory actions to keep the performance of the system as high as possible while the system is running. We show on two activity datasets with different configurations of on body sensors that detection and compensation of anomalies make the system more robust against the changes.