Sensor Failure Detection in Ambient Assisted Living Using Association Rule Mining.
Sensors (Basel)
; 20(23)2020 Nov 26.
Article
en En
| MEDLINE
| ID: mdl-33256000
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.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Vida Independiente
/
Inteligencia Ambiental
Tipo de estudio:
Diagnostic_studies
/
Risk_factors_studies
Límite:
Aged
/
Humans
Idioma:
En
Año:
2020
Tipo del documento:
Article