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False alarm detection using dynamic threshold in medical wireless sensor networks

  • S. SaraswathiEmail author
  • G. R. Suresh
  • Jeevaa Katiravan
Article
  • 11 Downloads

Abstract

Sensor networks suffer from various sensor faults and false measurements in healthcare application and this vulnerability of the delay should handle efficiently and timely response in various application of WSN. For instance, in healthcare application, the false measurements generate false alarms which require to take unnecessary action from the healthcare department. The quality of the health care service can improve in remote healthcare monitoring system by introducing a new approach to identify the true medical condition and differentiate true and false alarms. In this paper, we proposed a novel approach to analysis past historical data collected from various medical sensors to identify the sensor anomaly. The main goal of this approach is to differentiate between true and false alarms effectively. The proposed system analysis the historical data to predicts the sensor value which compares with real sensed values at a time incident. The dynamically adjust the threshold value by comparing the difference between predicted value and historic value to determine the anomaly of sensor value. This system has been worked on huge real-time healthcare dataset and result shows that the new approach has been applied on real healthcare datasets and result of this system shows the detection rate is high and false positive rate is low which conclude that this approach is very useful in WSN application such as health monitoring system and it will be competitive with others.

Keywords

Medical sensors Healthcare monitoring system Anomaly detection Prediction Sensor fault True alarm Correlation Feature extraction Dynamic threshold 

Notes

Compliance with ethical standards

Conflict of interest

Authors declares no conflict of interest.

Ethical statement

This article does not contain any studies with animals performed by any of the authors.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • S. Saraswathi
    • 1
    Email author
  • G. R. Suresh
    • 2
  • Jeevaa Katiravan
    • 3
  1. 1.Department of CSEPanimalar Engineering CollegeChennaiIndia
  2. 2.Department of Biomedical EngineeringSt Peter’s Insititute of Higher Education & ResearchChennaiIndia
  3. 3.Department of ITVelammal Engineering CollegeChennaiIndia

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