Sensor Failure Detection in Ambient Assisted Living Using Association Rule Mining

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.

Barriers and Facilitators of Ambient Assisted Living Systems: A Systematic Literature Review

“… [ 79 ] S57 AnAbEL: Towards empowering people living with dementia in ambient assisted living Gimenez Manuel, J.G., Augusto, J.C., and Stewart, J. [ 80 ] S58 Sensor failure detection in ambient assisted living using association rule mining ElHady, N.E., Jonas, S., Provost, J., and Senner, V. [ 81 ] S59 Deliberative Context-Aware Ambient Intelligence System for Assisted Living Homes Babli, M., Rincon, J.A., Onaindia, E., Carrascosa, C., and Julian, V. [ 82 ] S60 Evaluating the impact of different symmetrical models of ambient assisted living systems Alosaimi, W., Ansari, M.T.J., Alharbi, A., Alyami, H., Seh, A.H., Pandey, A.K., Agrawal, A. and Khan, R.A. [ 83 ] S61 FriendCare-AAL: a robust social IoT-based alert generation system for ambient assisted living Gulati, N., and Kaur, P.D. [ 84 ] …”

Sensor Technology for Smart Homes

“…Detecting sensor failure as early as possible is essential for ensuring trustworthiness and confidence. In [ 1 ], a mechanism for sensor failure detection and subsequent sensor isolation based on association rule mining is proposed for event-driven, ambient sensors. Promising results have emerged in the cases of unlabelled datasets and unknown sensor topologies.…”

Design of a Kitchen-Monitoring and Decision-Making System to Support AAL Applications

“…It is especially important to recognize more complex activities that can only be detected by the joint use of different, and often very diverse, sensors [ 29 ]. In addition, advanced and intelligent mining techniques can be used to detect irregularities within a system or to detect the malfunction of individual components [ 31 ]. When it comes to activities in the kitchen, there are studies related to the recognition of activity such as eating [ 11 , 14 , 17 , 29 ], drinking [ 11 , 17 , 29 ], taking medication [ 14 , 17 , 29 ], and similar.…”