论文信息 - Learning User Models to Improve Wayfinding Assistance for Individuals with Cognitive Impairment

Learning User Models to Improve Wayfinding Assistance for Individuals with Cognitive Impairment

In this paper we discuss the benefits and challenges of designing customizable and adaptable applications for individuals to help improve their quality of life. As a supporting example, we describe our work learning user models to produce tailored pedestrian wayfinding directions for individuals with cognitive impairments.

[1] Rogerio DePaula,et al. A Framework for the Adoption of Assistive Technology , 2002 .

[2] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[3] Gerhard Fischer,et al. Socio-technical environments supporting people with cognitive disabilities using public transportation , 2005, TCHI.

[4] Anind K. Dey,et al. Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior , 2008, UbiComp.

[5] Krzysztof Z. Gajos,et al. Improving the performance of motor-impaired users with automatically-generated, ability-based interfaces , 2008, CHI.

[6] Henry A. Kautz,et al. Informing the design of an automated wayfinding system for individuals with cognitive impairments , 2009, 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare.

[7] Henry A. Kautz,et al. Customizing directions in an automated wayfinding system for individuals with cognitive impairment , 2009, Assets '09.