Abstract. Dementia occurs much more frequently in the elders who exhibit impairments of memory, thought and reasoning. In this paper, we present a hybrid context-aware reminding framework intended to help elders with mild dementia improve their level of independence and quality of life. Based on the user study in three different pilot sites, the reminding services are identified and classified into four types according to the nature and urgency. The framework with a novel scheduling mechanism is designed which handles both synchronous time-based and asynchronous event-based reminding services. In order to facilitate the interaction between the caregivers and system, we also provide a simple software tool for caregivers to create and edit the reminding services. Finally, we present some initial implementation results.

We present an autonomous assistive robotic system for human activity recognition from video sequences. Due to the large variability inherent to video capture from a nonfixed robot (as opposed to a fixed camera), as well as the robot's limited computing resources, implementation has been guided by robustness to this variability and by memory and computing speed efficiency. To accommodate motion speed variability across users, we encode motion using dense interest point trajectories. Our recognition model harnesses the dense interest point bag-of-words representation through an intersection kernel-based SVM that better accommodates the large intra-class variability stemming from a robot operating in different locations and conditions. To contextually assess the engine as implemented in the robot, we compare it with the most recent approaches of human action recognition performed on public datasets (non-robot-based), including a novel approach of our own that is based on a two-layer SVMhidden conditional random field sequential recognition model. The latter's performance is among the best within the recent state of the art. We show that our robot-based recognition engine, while less accurate than the sequential model, nonetheless shows good performances, especially given the adverse test conditions of the robot, relative to those of a fixed camera.

Persons suffering from mild dementia can benefit from a form of cognitive prosthetic which can be used to assist them with their day to day activities. Within our current work we are aiming to develop a successful user-validated cognitive prosthetic for persons with mild dementia. We have devised a three phased waterfall methodology to support our developments. Based on the evaluation of the first of these phases which involved the processes of user requirements gathering, prototype development and evaluation of in situ deployment of the technology we have been able to guide the technical development within the second phase of our work. Within this paper we provide an overview of the first phase of our methodology and demonstrate how we have used the results from this to guide the second phase of our work, especially with regards to the notion of personalisation.

In this paper, we propose a context-aware reminding system to assist dementia elders for their daily activities. We particularly focus on the issue of conflict detecting and handling in the real-world reminding applications. We adopt a planning approach to describe the constraints of reminders, and transform the constraints into temporal constraint graph for reminder conflict detecting. In real-time reminder scheduling, we define three disruptive activities, and present a context-aware reminding strategy to handle the conflicts between pre-planned and disruptive activities. Finally, the system prototype is introduced.

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