Abstract : Recognizing the activities of daily living plays an important role in healthcare. It is necessary to use an adapted model to simulate the human behavior in a domestic space to monitor the patient harmonically and to intervene in the necessary time. In this paper, we tackle this problem using the hierarchical hidden Markov model for representing and recognizing complex indoor activities. We propose a new grammar, called “Home By Room Activities Language”, to facilitate the complexity of human scenarios and consider the abnormal activities.
The International Conference on Sensor Networks (SENSORNETS'16), Feb 2016, rome, Italy. Proceedings of theInternational Conference on Sensor Networks (SENSORNETS 2016), 2016
https://hal.inria.fr/hal-01280001
Contributeur : Tayeb Lemlouma
<>
Soumis le : mardi 8 mars 2016 - 19:59:38
Dernière modification le : mardi 10 juillet 2018 - 15:46:49
Document(s) archivé(s) le : dimanche 13 novembre 2016 - 11:49:12
Zaineb Liouane, Tayeb Lemlouma, Philippe Roose, Frédéric Weis, Messaoud Hassani. A Markovian-based Approach for Daily Living Activities Recognition
. The International Conference on Sensor Networks (SENSORNETS'16), Feb 2016, rome, Italy. Proceedings of theInternational Conference on Sensor Networks (SENSORNETS 2016), 2016. 〈hal-01280001〉
