Enhancements in anomaly detection in body sensor networks

Title

Enhancements in anomaly detection in body sensor networks

Creator

Harry R.S.; Joseph R.

Description

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. 2019 IEEE.

Source

Proceedings - 22nd IEEE International Conference on Computational Science and Engineering and 17th IEEE International Conference on Embedded and Ubiquitous Computing, CSE/EUC 2019, pp. 384-389.

Date

2019-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Anomaly detection; Body sensor networks; Machine learning

Coverage

Harry R.S., Dept. of Electronics and Communications, Christ University, Bangalore, India; Joseph R., Dept. of Electrical Engineering, IIT Madras, Chennai, India

Rights

Restricted Access

Relation

ISBN: 978-172811663-1

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/CSE/EUC.2019.00079

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