Artificial immune system – a new approach for the long-term data monitoring in ambient assisted living

Sebastian Bersch, Djamel Azzi, Rinat Khusainov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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This paper makes a case for the use of Artificial Immune Systems (AIS) in the area of Ambient Assisted Living (AAL) for anomaly detection and long-term data monitoring. The literature review of relevant solutions developed for AAL and the use of AIS in other fields is presented. It is further highlighted that so far AIS have not been used in the area of AAL. To advocate the use of AIS in this area, the authors compare the accuracy rate of detecting abnormal activity between a simple Signal Vector Magnitude (SVM)-based threshold algorithm, two Artificial Immune System (AIS)-based monitoring algorithms, and four supervised classification algorithms (KNN, J48, Naïve Bayes, and SMO). The results of the comparison, using precision, recall, and fmeasure, showed good results for the two different AIS-based monitoring algorithms, warranting current and future work.
Original languageEnglish
Title of host publicationSensor Systems and Software
Subtitle of host publication5th International Conference, S-CUBE 2014, Coventry, UK, October 6-7, 2014, revised selected papers
EditorsEiman Kanjo, Dirk Trossen
Number of pages10
ISBN (Electronic)9783319171364
ISBN (Print)9783319171357
Publication statusPublished - 6 May 2015
Event5th International Conference on Sensor Systems and Software - Coventry, United Kingdom
Duration: 6 Oct 20147 Oct 2014

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
PublisherSpringer International Publishing
ISSN (Print)1867-8211


Conference5th International Conference on Sensor Systems and Software
Abbreviated titleS-CUBE 2014
Country/TerritoryUnited Kingdom


  • artificial immune system
  • AIS
  • fall detection
  • abnormality detection
  • supervised classifier
  • unsupervised classifier


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