Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

Open Innovation: The New Imperative for Creating and Profiting from Technology

What Video Games Have to Teach Us about Learning and Literacy

In this provocative book, Barwise and Perry tackle the slippery subject of \"meaning, \" a subject that has long vexed linguists, language philosophers, and logicians.

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Situations and Attitudes.

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.

A Smart Home Agent for Plan Recognition of Cognitively-impaired Patients.

This article addresses the problem of recognizing the behavior of a person suffering from Alzheimer's disease at early-intermediate stages. We present a keyhole plan recognition model, based on lattice theory and action description logic, which transforms the recognition problem into a classification issue. This approach allows us to formalize the plausible incoherent intentions of the patient, resulting from the symptoms of his cognitive impairment, such as disorientation, memory lapse, etc. An implementation of this model was tested in our smart home laboratory, by simulating a set of real case scenarios.

A keyhole plan recognition model for alzheimer's patients: first results

The number of seniors and other people needing daily assistance continues to increase, but the current human resources available to achieve this in the coming years will certainly be insufficient. To remedy this situation, smart habitats have emerged as an innovative avenue for supporting needs of daily assistance. Smart homes aim to provide cognitive assistance in decision making by giving hints, suggestions, and reminders, with different kinds of effectors, to residents. To implement such technology, the first challenge to overcome is the recognition of ongoing activity. Some researchers have proposed solutions based on binary sensors or cameras, but these types of approaches infringed on residents' privacy. A new affordable activity-recognition system based on passive RFID technology can detect errors related to cognitive impairment. The entire system relies on an innovative model of elliptical trilateration with several filters, as well as on an ingenious representation of activities with spatial zones. The authors have deployed the system in a real smart-home prototype; this article renders the results of a complete set of experiments conducted on this new activity-recognition system with real scenarios.

Exploiting Passive RFID Technology for Activity Recognition in Smart Homes

To face new challenges caused by society aging, several researchers have initiated the experimentation of serious games as a re-education platform to help slowing down the decline of people suffering from Alzheimer. In the last few years, academic studies have been conducted and some commercial products (Nintendo's Brain Age, Big Brain Academy, etc.) have emerged. Nevertheless, these initiatives suffer from multiple important limitations since they do not really suit perceptual and interaction needs of silver-aged gamers, more specifically people suffering from Alzheimer disease. In an effort to address this important issue, we present in this paper a set of specific guidelines for designing and implementing effective serious games targeting silver-aged and Alzheimer's patients. Our guidelines cover the following aspects: (i) choosing right in-game challenges, (ii) designing appropriate interaction mechanisms for cognitively impaired people, (iii) implementing artificial intelligence for providing adequate assistive prompting and dynamic difficulty adjustments, (iv) producing effective visual and auditory assets to maximize cognitive training. Also, as a case study, we present the prototype of our new serious game for Alzheimer's patients.

Developing serious games specifically adapted to people suffering from alzheimer

Research on progressive dementia increased significantly in the past years due to the urgency of the aging population. Patients suffering from such dementia, for instance Alzheimer's disease, lose efficiency in cognitive spheres such as memory, planning skills, initiative and perseverance. Some researchers tried to evaluate the potential of close-to-reality simulations and generic video games for brain training to stimulate the cognitive abilities of AD patients. Using recent advances in artificial intelligence such as learning, activity recognition and guidance to enhance this concept of training, we are proposing, in this paper, a detailed explanation of an adapted serious game we designed for this purpose. A prototype has been developed showing how to exploit AI techniques to create an affordable and accessible tool for cognitive training and allowing in-game estimation of the patient's cognitive performance.

Bruno Bouchard

Papers

A Smart Home Agent for Plan Recognition of Cognitively-impaired Patients.

A keyhole plan recognition model for alzheimer's patients: first results

Exploiting Passive RFID Technology for Activity Recognition in Smart Homes

Developing serious games specifically adapted to people suffering from alzheimer

Serious games in cognitive training for Alzheimer's patients