Enhancements in anomaly detection in body sensor networks

Anomaly detection in Body Sensor Networks (BSNs), have recently received much attention from the healthcare community. This is partly due to the development of sensor based real-time tracking and monitoring networks. These networks have been responsible not only for ensuring critical medical treatment at times of emergency, but have also made it easier for health-care personnel to administer critical treatment. In this paper we consider improvements to existing machine learning methods that detect anomalous sensor measurements. The improved methods are a step in the right direction in ensuring unduly overheads due to faulty sensors don't interfere while administering life-critical treatment in a limited resources scenario.