Wireless Body Area Network Sensor Faults and Anomalous Data Detection and Classification using Machine Learning

Abstract

Sensor Networks are very much vulnerable and prone to faults and external attacks. Sensor networks used for Healthcare Monitoring are termed as Wireless Body Area Networks (WBAN), which is used for collecting various vital physiological parameters of patients from remote locations. However, WBAN sensors are prone to failures because of noise, hardware misplacement, patient‘s sweating. Sensed data from these sensors are sent from the Local Processing Unit to Medical Professionals. It would be very difficult for the Medical Professionals to diagnose correctly if the sensed data from these sensors are faulty or effected by the malicious third party. At times, even faulty data might lead to misdiagnosis or death of a patient. It motivated us to address this challenge by proposing a Machine Learning Paradigm to distinguish this anomalous data from the genuine sensed data. Firstly, we classify the health parameters as normal records or abnormal record. After the classification, we propose to apply regression technique for identifying the anomalous data and actual critical data. We use real patient‘s vital physiological parameters for validating the robustness and reliability of our proposed approach.