A Markovian-based Approach for Daily Living Activities Recognition

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.