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Activity recognition using onbody sensors are prone to degradation due to changes on sensor readings. The changes can occur because of degradation or alteration in the behavior 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 onbody sensors that detection and compensation of anomalies make the system more robust against the changes.