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Conference Papers Year : 2013

Combining Multiple Sensors for Event Recognition of Older People

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We herein present a hierarchical model-based framework for event recognition using multiple sensors. Event models combine a priori knowledge of the scene (3D geometric and semantic information, such as contextual zones and equipments) with moving objects (e.g., a Person) detected by a monitoring system. The event models follow a generic ontology based on natural language; which allows domain experts to easily adapt them. The framework novelty relies on combining multiple sensors (heterogeneous and homogeneous) at decision level explicitly or implicitly by handling their conflict using a probabilistic approach. The implicit event conflict handling works by computing the event reliabilities for each sensor, and then combine them using Dempster-Shafer Theory. The multi-sensor system is evaluated using multi-modal recording of instrumental daily living activities (e.g., watching TV, writing a check, preparing tea, organizing the week intake of prescribed medication) of participants of a clinical study of Alzheimer's disease. The evaluation presents the preliminary results of this approach on two cases: the combination of events from heterogeneous sensors (a RGB camera and a wearable inertial sensor); and the combination of conflicting events from video cameras with a partially overlapped field of view (a RGB- and a RGB-D-camera). The results show the framework improves the event recognition rate in both cases.
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hal-00907033 , version 1 (20-11-2013)



Carlos Fernando Crispim-Junior, Qiao Ma, Baptiste Fosty, Rim Romdhane, François Bremond, et al.. Combining Multiple Sensors for Event Recognition of Older People. MIIRH - 1st ACM MM Workshop on Multimedia Indexing and information Retrieval for Healthcare - 2013, Oct 2013, Barcelona, Spain. pp.15-22, ⟨10.1145/2505323.2505329⟩. ⟨hal-00907033⟩
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