Generating Bayesian Network Structures for Self-diagnosis of Sensor Networks in the Context of Ambient Assisted Living for Aging Well

Oliveira, Camila Helena Souza; Giroux, Sylvain; Ngankam, Hubert Kenfack; Pigot, Hélène

doi:10.1007/978-3-319-66188-9_17

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…A self-diagnosis framework was proposed by Oliveira et al [ 35 ], where a Bayesian network construction algorithm is used to create a Bayesian network for each scenario that is supposed to be fulfilled by the AAL system to assist the user. The algorithm takes as inputs the rules file that specifies the causal relations between variables, and the scenario description file that specifies the required assistance and the home description.…”

Section: Literature Survey Resultsmentioning

confidence: 99%

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A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living

ElHady

Provost

2018

Sensors

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Ambient Assisted Living (AAL) systems aim to enable the elderly people to stay active and live independently into older age by monitoring their behaviour, provide the needed assistance and detect early signs of health status deterioration. Non-intrusive sensors are preferred by the elderly to be used for the monitoring purposes. However, false positive or negative triggers of those sensors could lead to a misleading interpretation of the status of the elderlies. This paper presents a systematic literature review of the sensor failure detection and fault tolerance in AAL equipped with non-intrusive, event-driven, binary sensors. The existing works are discussed, and the limitations and research gaps are highlighted.

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Section: Literature Survey Resultsmentioning

confidence: 99%

“…The reviewed fault detection and diagnosis frameworks [ 33 , 34 , 35 ] were designed to only suit AAL systems involved with sensor-actuator feedback.…”

Section: Discussionmentioning

confidence: 99%

A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living

ElHady

Provost

2018

Sensors

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“…The proposed model-based sensor failure detection approaches are not promising as they either use unrealistic models of resident motion that do not take into consideration previous locations and speed or install extra hardware that increases cost as well as the chances of errors. Fault detection and diagnosis frameworks that rely on modelling the sensors' and actuators' activation due to various user scenarios were presented in [20][21][22]. However, it can only detect failures in sensors that are involved in tasks that have sensor-actuator feedback.…”

Section: Related Workmentioning

confidence: 99%

Sensor Failure Detection in Ambient Assisted Living Using Association Rule Mining

ElHady

Jonas

Provost

et al. 2020

Sensors

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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.

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Generating Bayesian Network Structures for Self-diagnosis of Sensor Networks in the Context of Ambient Assisted Living for Aging Well

Cited by 2 publications

References 7 publications

A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living

A Systematic Survey on Sensor Failure Detection and Fault-Tolerance in Ambient Assisted Living

Sensor Failure Detection in Ambient Assisted Living Using Association Rule Mining

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