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  1. Robles, A. (Ed.)
    Although various navigation apps are available, people who are blind or have low vision (PVIB) still face challenges to locate store entrances due to missing geospatial information in existing map services. Previously, we have developed a crowdsourcing platform to collect storefront accessibility and localization data to address the above challenges. In this paper, we have significantly improved the efficiency of data collection and user engagement in our new AI-enabled Smart DoorFront platform by designing and developing multiple important features, including a gamified credit ranking system, a volunteer contribution estimator, an AI-based pre-labeling function, and an image gallery feature. For achieving these, we integrate a specially designed deep learning model called MultiCLU into the Smart DoorFront. We also introduce an online machine learning mechanism to iteratively train the MultiCLU model, by using newly labeled storefront accessibility objects and their locations in images. Our new DoorFront platform not only significantly improves the efficiency of storefront accessibility data collection, but optimizes user experience. We have conducted interviews with six adults who are blind to better understand their daily travel challenges and their feedback indicated that the storefront accessibility data collected via the DoorFront platform would be very beneficial for them. 
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    Free, publicly-accessible full text available June 1, 2024
  2. Santiago, J. (Ed.)
    The storefront accessibility can substantially impact the way people who are blind or visually impaired (BVI) travel in urban environments. Entrance localization is one of the biggest challenges to the BVI people. In addition, improperly designed staircases and obstructive store decorations can create considerable mobility challenges for BVI people, making it more difficult for them to navigate their community hence reducing their desire to travel. Unfortunately, there are few approaches to acquiring this information in advance through computational tools or services. In this paper, we propose a solution to collect large- scale accessibility data of New York City (NYC) storefronts using a crowdsourcing approach on Google Street View (GSV) panoramas. We develop a web-based crowdsourcing application, DoorFront, which enables volunteers not only to remotely label storefront accessibility data on GSV images, but also to validate the labeling result to ensure high data quality. In order to study the usability and user experience of our application, an informal beta-test is conducted and a user experience survey is designed for testing volunteers. The user feedback is very positive and indicates the high potential and usability of the proposed application. 
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  3. This paper describes the interface and testing of an indoor navigation app - ASSIST - that guides blind & visually impaired (BVI) individuals through an indoor environment with high accuracy while augmenting their understanding of the surrounding environment. ASSIST features personalized inter-faces by considering the unique experiences that BVI individuals have in indoor wayfinding and offers multiple levels of multimodal feedback. After an overview of the technical approach and implementation of the first prototype of the ASSIST system, the results of two pilot studies performed with BVI individuals are presented. Our studies show that ASSIST is useful in providing users with navigational guidance, improving their efficiency and (more significantly) their safety and accuracy in wayfinding indoors. 
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  4. The goal of the proposed project is to transform a large transportation hub into a smart and accessible hub (SAT-Hub), with minimal infrastructure change. The societal need is significant, especially impactful for people in great need, such as those who are blind and visually impaired (BVI) or with Autism Spectrum Disorder (ASD), as well as those unfamiliar with metropolitan areas. With our inter-disciplinary background in urban systems, sensing, AI and data analytics, accessibility, and paratransit and assistive services, our solution is a hu-man-centric system approach that integrates facility modeling, mobile navigation, and user interface designs. We leverage several transportation facili-ties in the heart of New York City and throughout the State of New Jersey as testbeds for ensuring the relevance of the research and a smooth transition to real world applications. 
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  5. Blind & visually impaired (BVI) individuals and those with Autism Spectrum Disorder (ASD) each face unique challenges in navigating unfamiliar indoor environments. In this paper, we propose an indoor positioning and navigation system that guides a user from point A to point B indoors with high accuracy while augmenting their situational awareness. This system has three major components: location recognition (a hybrid indoor localization app that uses Bluetooth Low Energy beacons and Google Tango to provide high accuracy), object recognition (a body-mounted camera to provide the user momentary situational awareness of objects and people), and semantic recognition (map-based annotations to alert the user of static environmental characteristics). This system also features personalized interfaces built upon the unique experiences that both BVI and ASD individuals have in indoor wayfinding and tailors its multimodal feedback to their needs. Here, the technical approach and implementation of this system are discussed, and the results of human subject tests with both BVI and ASD individuals are presented. In addition, we discuss and show the system’s user-centric interface and present points for future work and expansion. 
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  6. Blind & visually impaired individuals often face challenges in wayfinding in unfamiliar environments. Thus, an accessible indoor positioning and navigation system that safely and accurately positions and guides such individuals would be welcome. In indoor positioning, both Bluetooth Low Energy (BLE) beacons and Google Tango have their individual strengths but also have weaknesses that can affect the overall usability of a system that solely relies on either component. We propose a hybrid positioning and navigation system that combines both BLE beacons and Google Tango in order to tap into their strengths while minimizing their individual weaknesses. In this paper, we will discuss the approach and implementation of a BLE- and Tango-based hybrid system. The results of pilot tests on the individual components and a human subject test on the full BLE and hybrid systems are also presented. In addition, we have explored the use of vibrotactile devices to provide additional information to a user about their surroundings. 
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