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

Project summary:

People with vision impairment are less confident about traveling alone in an unfamiliar environment largely due to uncertainty and insufficient accessible information in such an environment. In order to improve mobility, accessibility, and their level of confidence in using the transportation system, it is important to remove information barriers that could potentially impede their mobility. A "condition aware" infrastructure using the Bluetooth low energy (BLE) technology was developed to ensure that the information is up-to-date. This will ensure that correct audible information is provided to users at the right location. A Multivariable Regression (MR) algorithm using 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 reduces the environmental uncertainty and dynamic nature of RSS measurements in a Bluetooth network. The range output from the MR-SVD model is then integrated with an extended Kalman filter to provide positioning and mapping solutions. Using 6 BLE beacons at an intersection in St. Paul, MN, this approach achieves 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 are able to successfully detect if the location of one or multiple BLE beacons in a network is changed based on Bluetooth RSS indications. With the self-monitoring network, it is possible to ensure that information associated with each Bluetooth beacon is provided to the visually impaired at the right location to support their wayfinding in a transportation network.

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