Continuous and accurate smartphone-based localization is a promising technology for supporting independent mobility of people with visual impairments. However, despite extensive research on indoor localization techniques, they are still not ready for deployment in large and complex environments, like shopping malls and hospitals, where navigation assistance is needed. To achieve accurate, continuous, and real-time localization with smartphones in such environments, we present a series of key techniques enhancing a probabilistic localization algorithm. The algorithm is designed for smartphones and employs inertial sensors on a mobile device and Received Signal Strength (RSS) from Bluetooth Low Energy (BLE) beacons. We evaluate the proposed system in a 21,000 m 2 shopping mall which includes three multi-story buildings and a large open underground passageway. Experiments in this space validate the effect of the proposed technologies to improve localization accuracy. Field experiments with visually impaired participants confirm the practical performance of the proposed system in realistic use cases.

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a b s t r a c tIndependent mobility involves a number of challenges for people with visual impairment or blindness. In particular, in many countries the majority of traffic lights are still not equipped with acoustic signals. Recognizing traffic lights through the analysis of images acquired by a mobile device camera is a viable solution already experimented in scientific literature. However, there is a major issue: the recognition techniques should be robust under different illumination conditions. This contribution addresses the above problem with an effective solution: besides image processing and recognition, it proposes a robust setup for image capture that makes it possible to acquire clearly visible traffic light images regardless of daylight variability due to time and weather. The proposed recognition technique that adopts this approach is reliable (full precision and high recall), robust (works in different illumination conditions) and efficient (it can run several times a second on commercial smartphones). The experimental evaluation conducted with visual impaired subjects shows that the technique is also practical in supporting road crossing.

Continuous, accurate, and real-time smartphone-based localization is a promising technology for supporting independent mobility of people with visual impairments. However, despite extensive research on indoor localization techniques, localization technologies are still not ready for deployment in large and complex environments such as shopping malls and hospitals, where navigation assistance is needed most. We identify six key challenges for accurate smartphone localization related to the large-scale nature of the navigation environments and the user's mobility. To address these challenges, we present a series of techniques that enhance a probabilistic localization algorithm. The algorithm utilizes mobile device inertial sensors and Received Signal Strength (RSS) from Bluetooth Low Energy (BLE) beacons. We evaluate the proposed system in a 21,000 m 2 shopping mall that includes three multi-story buildings and a large open underground passageway. Experiments conducted in this environment demonstrate the effectiveness of the proposed technologies to improve localization accuracy. Field experiments with visually impaired participants confirm the practical performance of the proposed system in realistic use cases.

People with visual impairments often have to rely on the assistance of sighted guides in airports, which prevents them from having an independent travel experience. In order to learn about their perspectives on current airport accessibility, we conducted two focus groups that discussed their needs and experiences in-depth, as well as the potential role of assistive technologies. We found that independent navigation is a main challenge and severely impacts their overall experience. As a result, we equipped an airport with a Bluetooth Low Energy (BLE) beacon-based navigation system and performed a real-world study where users navigated routes relevant for their travel experience. We found that despite the challenging environment participants were able to complete their itinerary independently, presenting none to few navigation errors and reasonable timings. This study presents the frst systematic evaluation posing BLE technology as a strong approach to increase the independence of visually impaired people in airports. CCS CONCEPTS • Human-centered computing → Empirical studies in accessibility; Accessibility technologies.