Hierarchical Hidden Markov Model in Detecting Activities of Daily Living in Wearable Videos for Studies of DementiaReport as inadecuate




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1 LaBRI - Laboratoire Bordelais de Recherche en Informatique 2 IMS - Laboratoire de l-intégration, du matériau au système 3 IRIT - Institut de recherche en informatique de Toulouse 4 ISPED - Institut de Santé Publique, d-Epidémiologie et de Développement 5 Epidémiologie et Biostatistique Bordeaux

Abstract : This paper presents a method for indexing activities of daily living in videos obtained from wearable cameras. In the context of dementia diagnosis by doctors, the videos are recorded at patients- houses and later visualized by the medical practitioners. The videos may last up to two hours, therefore a tool for an efficient navigation in terms of activities of interest is crucial for the doctors. The specific recording mode provides video data which are really difficult, being a single sequence shot where strong motion and sharp lighting changes often appear. Our work introduces an automatic motion based segmentation of the video and a video structuring approach in terms of activities by a hierarchical two-level Hidden Markov Model. We define our description space over motion and visual characteristics of video and audio channels. Experiments on real data obtained from the recording at home of several patients show the difficulty of the task and the promising results of our approach.

Keywords : Activities of Daily Living Wearable Videos Video Indexing Hidden Markov Model





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Source: https://hal.archives-ouvertes.fr/



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