About one third of home-dwelling older people suffer a fall each year. The most painful falls occur when the person is alone and unable to get up, resulting in huge number of elders which are associated with institutionalization and high morbidity-mortality rate. The PAL (Personally Assisted Living) system appears to be one of the solutions of this problem. This ambient intelligence system allows elderly people to live in an intelligent and pro-active environment. It is charged with the supervision and control of the entrusted space, monitoring events and detecting falls, recognizing human activities through a network sensors, and finally providing support through robotic actuators. Such services have the potential of increasing autonomy of elders while minimizing the risks of living alone. This thesis describes the ongoing work of in-home elder tracking, activities daily living recognition, and automatic fall detection system using a set of non-intrusive sensors that grants privacy and comfort to the elders. In addition, a fault-tolerant fusion method is proposed using a purely informational formalism: information filter on the one hand, and information theory tools on the other hand. Residues based on the Kullback-Leibler divergence are used. Using an appropriate thresholding, these residues lead to the detection and the exclusion of sensors faults. The proposed algorithms were validated with many different scenarios containing the different activities: walking, sitting, standing, lying down, and falling. The performances of the developed methods showed a sensitivity of more than 94% for the fall detection of persons and more than 92% for the discrimination between the different ADLs (Activities of the daily life).
- Directeurs de thèse : Maan El Badaoui El Najjar / Francois Charpillet - Rapporteurs : Véronique Berge-Cherfaoui / Ghaleb Hoblos - Examinateurs : Claude Delpha / Ahmad Diab / Mohamad Khalil / Christine Perret-Guillaume
français
English