BACKGROUND: Embodied conversational agents (ECA) are possible enablers of assistive technologies, in particular for older adults with cognitive impairment. Yet, dedicated interaction management techniques addressing the specificities of this public are needed. OBJECTIVES: We assess whether the interaction management framework of the LOUISE (Lovely User Interface for Servicing Elders) ECA has the potential to overcome the user interface constraints linked to cognitive impairment. METHODS: LOUISE supports key target-specific features: personalization; attention management; context reminders; image and video displays; a conversation manager for task-oriented interactions; and the foundations for a domain-specific XML-based language for task-oriented assistive scenarios. LOUISE's usability and acceptance were evaluated at the Broca geriatric hospital in Paris. with a group of 14 older adults with either mild cognitive impairment (MCI) or Alzheimer's disease (AD) through four simple but realistic assistive scenarios: drinking, taking medicine, measuring blood pressure and choosing the lunch menu. RESULTS: Most 1 of our participants were able to interact with the ECA, succeeded in completing the proposed tasks and enjoyed our design. CONCLUSION: The field usability evaluation of LOUISE's interaction management framework suggests that this suite of interaction techniques can be effective in enabling interfaces for users with MCI or AD.
Embodied conversational agents (ECAs) are virtual characters using verbal and non-verbal communication for Human-machine interaction. The aim of our research is to create an ECA-based user interface for assistive technologies targeting older adults with cognitive impairment. Our design methodology is a co-design living lab approach, collecting design guidelines through questionnaires, focus groups and user trials.In this paper, we report on the results of the first phase of this iterative design process. We developed Louise, a semi-automatic ECA prototype that aims to compensate, through attention monitoring, for a user's attentional disorders by performing autonomous prompting, i.e., calling the user to regain his or her attention in case he or she got distracted. We evaluated the performance of Louise with a group of experts in assistive technologies and collected their feedback. Louise's simple attention estimator is more than 80% accurate. The system got quite positive reviews from users.
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