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Anomaly Detection in Activities of Daily Living Using One-Class Support Vector Machine

  • Salisu Wada Yahaya
  • Caroline Langensiepen
  • Ahmad Lotfi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 840)

Abstract

Different computational methodologies for anomaly detection has been studied in the past. Novelty detection involves classifying if test data differs from the training data. This is applicable to a scenario when there are sufficiently many normal training samples and little or no abnormal data. In this research, a novelty detection algorithm known as One-Class Support Vector Machine (SVM) is applied for detection of anomaly in Activities of Daily Living (ADL), specifically sleeping patterns, which could be a sign of Mild Cognitive Impairment (MCI) in older adults or other health-related issues. Tests conducted on both synthetic and real data shows promising results.

Keywords

Novelty detection Anomaly detection One-class SVM Activities of Daily Living (ADL) 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Salisu Wada Yahaya
    • 1
  • Caroline Langensiepen
    • 1
  • Ahmad Lotfi
    • 1
  1. 1.School of Science and TechnologyNottingham Trent UniversityNottinghamUK

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