In this article, we describe the latest version of Sibylle, an AAC system that permits persons who have severe physical disabilities to enter text with any computer application, as well as to compose messages to be read out through speech synthesis. The system consists of a virtual keyboard comprising a set of keypads that allow for the entering of characters or full words by a single-switch selection process. It also includes a sophisticated word prediction component which dynamically calculates the most appropriate words for a given context. This component is auto-adaptive, that is, it learns with every text the user enters. It thus adapts its predictions to the user's language and the current topic of communication as well. So far, the system works for French, German and English. Earlier versions of Sibylle have been used since 2001 in a rehabilitation center (Kerpape, France).

The SAM system was stable and reliable: both patients and control participants experienced few failures when completing the various stages of the scenarios. The graphic interface was effective for selecting and grasping the object – even in the absence of visual control. Users and carers were generally satisfied with SAM, although only a quarter of patients said that they would consider using the robot in their activities of daily living.

Automation of smart home for ambient assisted living is currently based on a widespread use of sensors. As efficient as it seems to be, this solution can sometimes be problematic when one focus on user acceptability intimately related to cost and intrusivity. In this paper, we propose a context-aware system based on the semantic analysis of each user request at runtime. Our goal is to infer user data usually sensored by using advanced semantic web tools to adapt home automation services to people with special needs. To take up this challenge, an ontology, automatically derived from a model-driven process, firstly defines usersystem interactions. Then, the use of rules allows an inference engine to deduce user location and intention leading to adapted service delivery.

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