A Positioning and Mapping Methodology Using Bluetooth and Smartphone Technologies to Support Situation Awareness and Wayfinding for the Visually Impaired

People with vision impairment often face challenges while traveling in an unfamiliar environment largely due to uncertainty and insufficient accessible information. To improve mobility, accessibility, and the level of confidence the visually impaired experience in using the transportation system, it is important to remove information barriers that could potentially impede their mobility. A "condition aware" infrastructure using Bluetooth low- energy (BLE) technology was developed to provide up-to-date and correct audible information to users at the right location. A Multivariable Regression (MR) algorithm using the Singular Value Decomposition (SVD) technique was introduced to model the relationship between Bluetooth Received Signal Strength (RSS) and the actual ranging distance in an outdoor environment. This methodology reduced the environmental uncertainty and dynamic nature of RSS measurements in a Bluetooth network. The range output from the MR-SVD model was integrated with an extended Kalman filter to provide positioning and mapping solutions. Using 6 BLE beacons at an intersection in St. Paul, Minnesota, our approach achieved an average position accuracy of 2.5 m and 3.8 m in X and Y directions, respectively. A few statistical techniques were implemented and were able to successfully detect whether the location of one or multiple BLE beacons in a network changed based on Bluetooth RSS indications. With the self-monitoring network, information associated with each Bluetooth beacon can be provided to the visually impaired at the right location to support their wayfinding in a transportation network.