An evidential approach for detection of abnormal behaviour in the presence of unreliable sensors |
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Authors: | Bruno Marhic,Laurent DelahocheClé ment Solau,Anne Marie Jolly-DesodtVincent Ricquebourg |
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Affiliation: | a Laboratoire des Technologies Innovantes (LTI) EA 3899, Dpt. Informatique, Avenue des Facultés le Bailly, 80000 Amiens Cedex 1, France b PRISME - Ecole Polytech’Orléans, 8 rue Leonard de Vinci - 45072 Orléans Cedex 2, France |
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Abstract: | We address the problem of abnormal behaviour recognition of the inhabitant of a smart home in the presence of unreliable sensors. The corner stone of this work is a two-level architecture sensor fusion based on the Transferable Belief Model (TBM). The novelty of our work lies in the way we detect both unreliable sensors and abnormal behaviour within our architecture by using a temporal analysis of conflict resulting from the fusion of sensors. Detection of abnormal behaviour is based on a prediction/observation process and the influence of the faulty sources is discarded by discounting coefficients. Our architecture is tested in a real-life setting using three heterogeneous sensors enabling the detection of impossible transitions between three possible postures: Sitting, Standing and Lying. The impact of having a faulty sensor management is also tested in the real-life experiment for posture detection. |
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Keywords: | Data fusion Transferable Belief Model Conflict analysis Abnormal behaviour Sensors diagnostics Sensor reliability |
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