iASSIST: An iPhone-Based Multimedia Information System for Indoor Assistive Navigation

Zhigang Zhu, Jin Chen, Lei Zhang, Yaohua Chang, Tyler Franklin, Hao Tang and Arber Ruci
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Zhigang Zhu: The City College, The City University of New York, USA
Jin Chen: The City College, The City University of New York, USA
Lei Zhang: Baruch College, The City University of New York, USA
Yaohua Chang: The City College, The City University of New York, USA
Tyler Franklin: The City College, The City University of New York, USA
Hao Tang: Borough of Manhattan Community College, The City University of New York, USA
Arber Ruci: New York City Regional Innovation Node, The City University of New York, USA

International Journal of Multimedia Data Engineering and Management (IJMDEM), 2020, vol. 11, issue 4, 38-59

Abstract: The iASSIST is an iPhone-based assistive sensor solution for independent and safe travel for people who are blind or visually impaired, or those who simply face challenges in navigating an unfamiliar indoor environment. The solution integrates information of Bluetooth beacons, data connectivity, visual models, and user preferences. Hybrid models of interiors are created in a modeling stage with these multimodal data, collected, and mapped to the floor plan as the modeler walks through the building. Client-server architecture allows scaling to large areas by lazy-loading models according to beacon signals and/or adjacent region proximity. During the navigation stage, a user with the navigation app is localized within the floor plan, using visual, connectivity, and user preference data, along an optimal route to their destination. User interfaces for both modeling and navigation use multimedia channels, including visual, audio, and haptic feedback for targeted users. The design of human subject test experiments is also described, in addition to some preliminary experimental results.

Date: 2020
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International Journal of Multimedia Data Engineering and Management (IJMDEM) is currently edited by Chengcui Zhang

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