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              <text>Enhancements in anomaly detection in body sensor networks</text>
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              <text>Anomaly detection; Body sensor networks; Machine learning</text>
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              <text>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.</text>
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              <text>Harry R.S.; Joseph R.</text>
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              <text>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.</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>&lt;a href="https://doi.org/10.1109/CSE/EUC.2019.00079" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/CSE/EUC.2019.00079&lt;/a&gt;
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              <text>ISBN: 978-172811663-1</text>
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              <text>Harry R.S., Dept. of Electronics and Communications, Christ University, Bangalore, India; Joseph R., Dept. of Electrical Engineering, IIT Madras, Chennai, India</text>
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