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Alzheimer's disease is a degenerative disease characterised by a progressive loss of cognitive functions and impairment of activities of daily living severe enough to interfere with normal functioning. To help persons with this disease perform a variety of activities, our research team developed AP@LZ, an electronic organiser specifically designed for them. Two participants with Alzheimer's disease learned how to use AP@LZ in their daily lives by following a structured learning method. After the learning phase, the participants were able to use AP@LZ efficiently and facilitate their day-to-day activities for several months, despite the steady progression of the disease. These results suggest that persons with Alzheimer's disease can learn to use new technologies to compensate for their everyday memory problems, which opens up new rehabilitation possibilities in dementia care.

Despite technological limitations and the novelty of the field, smart home technologies represent a promising potential for the early screening of MCI and could support clinicians in geriatric care.

New learning in semantic dementia (SD) seems to be tied to a specific temporal and spatial context. Thus, cognitive rehabilitation could capitalise upon preserved episodic memory and focus on everyday activities which, once learned, will have an impact in everyday life. This pilot study thus explores the effectiveness of an ecological approach in one patient suffering from SD. EC, a 68-year-old woman with SD, stopped cooking complex meals due to a substantial loss of knowledge related to all food types. The therapy consisted of preparing a target recipe. She was asked to generate semantic attributes of ingredients found in one target, one control and two no-therapy recipes. The number of recipes cooked by EC between therapy sessions was computed. She was also asked to prepare a generalisation recipe combining ingredients from the target and control recipes. EC's generated semantic attributes (GSA) of ingredients pertaining to the target and control recipes increased significantly (p < .001), compared to the no-therapy recipes (ps > .79). The proportion of meals cooked also increased significantly (p = .021). For the generalisation recipe, she could not succeed without assistance. Frequent food preparation may have provided EC with new memories about the context, usage and appearance of some concepts. These memories seem very context-bound, but EC nonetheless re-introduced some recipes into her day-to-day life. The impact of these results on the relationship between semantic, episodic and procedural memory is discussed, as well as the relevance of an ecological approach in SD.

Dementia causes cognitive deficits producing functional impairments. Continuous care and monitoring are thus compulsory to keep at home elders suffering from dementia. Intelligent habitat can play a central role toward a global and integrated solution and alleviate relatives from the care burden. The general idea is twofold. On the one hand, the physical environment could supplement elder cognitive impairments by providing personalized environmental cues that assist him in achieving his tasks. On the other hand, the intelligent house could maintain a link with relatives and medical care system to inform them of the evolution of the disease and to alert them in case of emergency. This paper shows how intelligent houses can deliver such cognitive assistance to elders, prolonging the time they can remain at home. First we derive the requirements for cognitive assistance by an intelligent habitat from the impact of the Alzheimer disease in the daily living of elders. Subsequently we describe the layered computer infrastructure needed to implement a distributed intelligent house information system. The implementation of such a pervasive system raises many issues that are not trivial from a computer science perspective. In this paper, we focus on modelling issues. Finally a simple scenario is used to exemplify the interactions between the intelligent house and the elders.

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<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt;" align="left"><span class="text"><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;">Assistance to people suffering from cognitive deficiencies in a smart home raises complex issues. Plan recognition is one of them. We propose a formal framework for the recognition process based on lattice theory and action description logic. The framework minimizes the uncertainty about the prediction of the observed agent’s behaviour by dynamically generating new implicit extra-plans. This approach offers an effective solution to actual plan recognition problem in a smart home, in order to provide assistance to persons suffering from cognitive deficits. An implementation of this model was incorporated in our smart home laboratory, in order to validate the approach. We currently planning the experimentation phase of the system, which will be based on a set of real case scenarios.</span></span><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;"></span></p>