Sensor failure detection in ambient assisted living using association rule mining

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9 Scopus citations

Abstract

Ambient Assisted Living (AAL) is becoming crucial to help governments face the consequences of the emerging ageing population. It aims to motivate independent living of older adults at their place of residence by monitoring their activities in an unobtrusive way. However, challenges are still faced to develop a practical AAL system. One of those challenges is detecting failures in non-intrusive sensors in the presence of the non-deterministic human behaviour. This paper proposes sensor failure detection and isolation system in the AAL environments equipped with event-driven, ambient binary sensors. Association Rule mining is used to extract fault-free correlations between sensors during the nominal behaviour of the resident. Pruning is then applied to obtain a non-redundant set of rules that captures the strongest correlations between sensors. The pruned rules are then monitored in real-time to update the health status of each sensor according to the satisfaction and/or unsatisfaction of rules. A sensor is flagged as faulty when its health status falls below a certain threshold. The results show that detection and isolation of sensors using the proposed method could be achieved using unlabelled datasets and without prior knowledge of the sensors’ topology.

Original languageEnglish
Article number6760
Pages (from-to)1-21
Number of pages21
JournalSensors (Switzerland)
Volume20
Issue number23
DOIs
StatePublished - 2 Nov 2020

Keywords

  • Ambient assisted living
  • Binary sensors
  • Enhanced living environments
  • Event-driven sensors
  • Fault detection
  • Fault isolation
  • Non-intrusive sensors
  • Sensor failure
  • Smart home

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