Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3336
Title: Wireless Body Area Network Sensor Faults and Anomalous Data Detection and Classification using Machine Learning
Authors: Nagdeo, Sumit Kumar
Mahapatro, Judhistir
Keywords: Sensor Networks
Wireless Body Area Network
Regression Model
True Positive Rate
False Positive Rate
Issue Date: Jul-2019
Citation: IEEE Bombay Section Signature Conference (IBSSC-2019), Mumbai, India, 26-28 July 2019
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.
Description: Copyright of this document belongs to proceedings publisher.
URI: http://hdl.handle.net/2080/3336
Appears in Collections:Conference Papers

Files in This Item:
File Description SizeFormat 
2019_IBSSC_JMahapatro_WirelessBody.pdfConference paper328.26 kBAdobe PDFView/Open    Request a copy


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.