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 prop...

In this work, we present the first results for neuralizing an Unsupervised Hidden Markov Model. We evaluate our approach on tag induction. Our approach outperforms existing generative models and is competitive with the state-of-the-art though with a simpler model easily extended to include additional context.

In this paper we propose a two-stage, supervised statistical model for detecting the activities of daily living (ADL) from sensor data streams. In the first stage each activity is modeled separately by a Markov model where sensors correspond to states. By modeling each sensor as a state we capture the absolute and relational temporal features of the atomic activities. A novel data segmentation ...