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Recent improvements in connected tools and learning algorithms allow new opportunities in the field of Ambient Assisted Living (AAL). However, smart home inhabitant's life habits are often required to obtain adequate results for energy management, security, Health at Home (HaH), and numerous other applications. In this paper, a model for life routines representation and algorithms for its generation is introduced. Study on the state of the art exposes that activity ordering and duration are key features of human behavior. Consequently, the presented approach focuses on a higher level of semantic by observing activities performed by the inhabitant rather than the sensor logs, which allow for better understanding of his comportment and universality of the model for multiple aims. Stochastic Time Automata (STA) is proposed as it adequately models activity ordering with probability associated to edges and activity duration through probability distribution associated to location delay. Presented approach does not require specific equipment besides sensors required for activity recognition and is versatile enough to be used in various applications. A case study highlights the relevancy of the chosen features and demonstrates that the proposed model is efficient to depict and understand inhabitants' life habits.

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World population ageing causes an important increase of people needing specific health care and monitoring. Dedicated institutions exist, but most of the elderly prefer to keep their autonomy for economic and personal reasons. To ensure a good quality of life and health to this population, Health at Home (HaH) solutions are explored. Many works focus on monitoring smart home inhabitant behavior to detect changes which might be due to health problems. These approaches are efficient to detect accident or short-term diseases such as a cold or influenza but tend to detect too tardily diseases which provoke slow declines in behavior. This is a problem as the elderly are likely to suffer from such troubles and early detection allows for better diagnosis and may help to prevent or reduce future worsening. In this paper, a novel approach for the detection of long-term behavior changes is introduced. It focuses on activity duration as this indicator is influenced by most diseases and give clear information about the inhabitant health status. This paper proposes data forecasting to detect future anomalies to assess existence of evolution in the current behavior. Information is sent to medical staff to refine their prognostic and adapt their treatment or call for a medical appointment. A case study based on a real smart home simulating a worst-case scenario attests for the efficiency of the approach and its resilience.