People with vision impairment usually use a white cane as their primary tool for wayfinding and obstacle detection. Environmental cues, though not always reliable, are used to support the decision making of the visually impaired at various levels of navigation and situation awareness. Due to differences in spatial perception as compared to sighted people, they often encounter physical as well as information barriers along a trip. In order to improve their mobility, accessibility and level of confidence in using our transportation system, it is important to remove not only the physical barriers but also the information barriers that could potentially impede their mobility and undermine safety. Many assistive systems have been developed in the past for visually impaired users to navigate and find their way. However, most of these systems were not adopted by users mostly due to the inconvenience of using such systems. In this research, we developed a mobile accessible information system that allows people with vision impairment to receive transportation information at key locations where decision making is necessary. A smartphone-based personal assistive system, called MAPS (Mobile Accessible Pedestrian System), was developed to provide intersection geometry and signal timing information, not available from other apps in the market for people with vision impairment. In addition, the MAPS incorporates a geospatial database with Bluetooth beacon information that allows the MAPS to provide navigation assistance, situation awareness, and wayfinding to users even when a GPS solution is not available. The MAPS app communicates with the traffic signal controller through a secured wireless link to obtain real-time Signal Phasing and Timing (SPaT) information, which together then inform visually impaired pedestrians with their current locations and when to cross streets. A self-monitoring infrastructure using a network of Bluetooth Low Energy (BLE) beacons was developed to ensure the information integrity of the network. The key contributions of this dissertation include the development of: • A smartphone-based navigation and decision support system that incorporates intersection geometry and traffic signal information for people with vision impairment, • A simple user’s interface (using a single or double-tap on a smartphone screen) that is easy for the visually impaired to learn and use, • Standardized message elements for an audible work zone bypass routing information system, • A self-monitoring infrastructure using a network of commercial off-the-shelf (COTS) low-cost BLE beacons, (including customized firmware allowing BLE beacons to monitor each other), • A crowdsourcing approach using users’ smartphones to monitor the status of BLE beacons and update messages associated with beacons, • A cloud-based geospatial database to support navigation by incorporating BLE beacon localization information when a GPS solution is not available, • A Singular Value Decomposition (SVD) based Multivariable Regression (MR) algorithm together with an Extended Kalman Filter (EKF) technique using beacon localization to provide a positioning solution by the smartphone even if a GPS solution is unavailable, and • Statistical methodologies and wireless signal fingerprinting techniques to monitor BLE beacons in a network in order to determine when a beacon is moved, removed or disappears. The intent of the MAPS is not to undermine the maintenance of skills and strategies that people with vision impairment have learned for navigation and wayfinding. Instead, the system aims to support their wayfinding capability, extend mobility and accessibility, and improve safety for the blind and visually impaired. This self-monitoring infrastructure ensures that correct information is provided to users at the right location when needed. This thesis also introduces the idea of using the same system to warn sighted pedestrians about approaching an intersection when they are distracted by looking at their smartphone.
University of Minnesota Ph.D. dissertation. May 2016. Major: Mechanical Engineering. Advisor: Max Donath. 1 computer file (PDF); xiv, 273 pages.
An Integrated Assistive System to Support Wayfinding and Situation Awareness for People with Vision Impairment.
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