Navigation Instruction Validation Tool and Indoor Wayfinding Training System for People with Disabilities

• Main Author: Ding, Linlin
• Format: Others
• Published: ScholarWorks@UMass Amherst 2017
• Subjects: Navigation Instruction Validation; Training Game; People with disabilities; Other Computer Engineering
• Online Access: https://scholarworks.umass.edu/masters_theses_2/575; https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1605&context=masters_theses_2

According to World Health Survey, there are 785 million (15.6%) people in the world that live with a disability. It is a well-known fact that lack of access to public transportation is a barrier for people with disabilities in seeking work or accessing health care. In this research, we seek to increase access to public transportation by introducing a virtual pre-travel training system that enables people with disabilities to get familiar with a public transportation venue prior to arriving at the venue. Using this system, users establish a mental map of the target environment prior to their arrival to the physical space, increasing their confidence and therefore increasing their chances of using public transportation. First, we have to guarantee that all navigation instructions sent to our training system are correct. Since the number of navigation instruction increases dramatically, instruction validation becomes a challenge. We propose a video game based validation tool which includes a game scene that represents in 2D the physical environment and uses a game avatar to verify the navigation instructions automatically in the game scene. The avatar traverses the virtual space following the corresponding navigation instructions. Only in case that it successfully reaches the planned destination, the current navigation instruction can be considered as correct. Then, we introduce a virtual reality based pre-travel wayfinding training system to assist people with disabilities to get familiar with a venue prior to their arrival at the physical space, which provides two modes: 1) Self-Guided mode in which the path between a source and a destination is shown to the user from third person perspective, and 2) Exploration mode in which the user explores and interacts with the environment. In the end, we have implemented visual analytics tools that track and evaluate trainees’ performance and help us optimize the game. These tools identify the difficulties faced by the trainees as well as obtain overall statistics on the trainees’ behavior in the indoor environment, helping us understand how to modify the system and adjust it to different classes of disabilities.