Temporal and Hierarchical HMM for Activity Recognition Applied in Visual Medical Monitoring using a Multi-Camera System

Abstract : We address in this paper an improved medical monitoring system through an automatic recognition of human activity in Intensive Care Units (ICUs). A multi camera vision system approach is proposed to collect video sequence for automatic analysis and interpretation of the scene. The latter is performed using Hidden Markov Model (HMM) with explicit state duration combine at the management of the hierarchical structure of the scenario. Significant experiments are carried out on the proposed monitoring system in a hospital's cardiology section in order to prove the need for computer-aided patient supervision to help clinicians in the decision making process. Temporal and hierarchical HMM handles explicitly the state duration and then provides a suitable solution for the automatic recognition of temporal events. Finally, the use of Temporal HMM (THMM) based approach improves the scenario recognition performance compared to the result of standard HMM models.
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Arnaud Ahouandjinou, Eugène C. Ezin, Cina Motamed. Temporal and Hierarchical HMM for Activity Recognition Applied in Visual Medical Monitoring using a Multi-Camera System. Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées, INRIA, 2015, 21, pp.49-66. ⟨hal-01304733⟩

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