Navigation and Obstacle Avoidance Help (NOAH) for elderly wheelchair users with cognitive impairment in long-term care

• Main Author: Viswanathan, Pooja
• Language: English
• Published: University of British Columbia 2012
• Online Access: http://hdl.handle.net/2429/42950

Cognitive impairments prevent older adults from using powered wheelchairs because of safety concerns, thus reducing mobility and resulting in increased dependence on caregivers. An intelligent powered wheelchair system (NOAH) is proposed to help restore mobility, while ensuring safety. Machine vision and learning techniques are described to help prevent collisions with obstacles, and provide reminders and navigation assistance through adaptive audio prompts. The intelligent wheelchair is initially tested in various controlled environments and simulated scenarios. Finally, the system is tested with older adults with mild-to-moderate cognitive impairment through a single-subject research design. Results demonstrate the high diversity of the target population, and highlight the need for customizable assistive technologies that account for the varying capabilities and requirements of the intended users. We show that the collision avoidance module is able to improve safety for all users by lowering the number of frontal collisions. In addition, the wayfinding module assists users in navigating along shorter routes to the destination. Prompting accuracy is found to be quite high during the study. While compliance with correct prompts is high across all users, we notice a distinct difference in the rates of compliance with incorrect prompts. Results show that users who are unsure about the optimal route rely more highly on system prompts for assistance, and thus are able to improve their wayfinding performance by following correct prompts. Improvements in wheelchair position estimation accuracy and joystick usability will help improve user performance and satisfaction. Further user studies will help refine user needs and hopefully allow us to increase mobility and independence of several elderly residents. === Science, Faculty of === Computer Science, Department of === Graduate