Ubiquitous Accessibility Digital-Maps for Smart Cities: Principles and Realization
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To support disabled individuals' active participation in the society, the Americans with Disabilities Act (ADA) requires installing various accessibility measures in roads and public accommodation spaces such as malls and airports. For example, curb ramps are installed on sidewalks to aid wheel-chaired individuals to transition from/to sidewalks smoothly. However, to comply with the ADA requirements, it is sufficient to have one accessible route in a place and usually there are no clear directions on how to reach that route. Hence, even within ADA-compliant facilities, accessing them can still be challenging for a disabled individual. To improve the spaces' accessibility, recently, systems have been proposed to rate outdoor walkways and intersections’ accessibility through active crowdsourcing where individuals mark and/or validate a maps’ accessibility assessments. Yet, depending on humans limits the ubiquity, accuracy and the update-rate of the generated maps. In this dissertation, we propose the AccessMap—Accessibility Digital Maps—system to build ubiquitous accessibility digital-maps automatically; where indoor/outdoor spaces are updated with various accessibility semantics and marked with assessment of their accessibility levels for the vision- and mobility-impairment disability types. To build the maps automatically, we propose a passive crowdsourcing approach where the users’ smartphone devices’ spatiotemporal sensors signals (e.g. barometer, accelerometer, etc.) are analyzed to detect and map the accessibility semantics. We present algorithms to passively detect various semantics such as accessible pedestrian signals and missing curb-ramps. We also present a probabilistic framework to construct the map while taking the uncertainty in the detected semantics and the sensors into account. AccessMap was evaluated in two different countries, the evaluation results show high detection accuracy for the different accessibility semantics. Moreover, the crowdsourcing framework helps further improve the map integrity overtime. Additionally, to tag the crowdsourced data with location stamps, GPS is the de-facto-standard localization method, but it fails in indoor environments. Thus, we present the Hapi WiFi-based localization system to estimate the crowdsourcers’ location indoors. WiFi represents a promising technology for indoor localization due to its world-wide deployment. Nevertheless, current systems either rely on a tedious expensive offline calibration phase and/or focus on a single-floor area of interest. To address these limitations, Hapi combines signal-processing, deep-learning and probabilistic models to estimate a user’s 2.5D location (i.e. the user floor-level and her 2D location within that floor) in a calibration-free manner. Our evaluation results show that, in high-rise buildings, we could achieve significant improvements over state-of-the-art indoor-localization systems.