Transportation Gaps and Disability-Related Unemployment: Smarter Cities and Wearables combating Commuting Challenges for the Visually Impaired
The pathologic link between vision loss and physical inactivity has confounded doctors, scientists, engineers, and patients for centuries. Mobility losses are extremely variable and not only tied to significant compromises in quality of life, but also to an abysmal unemployment rate. One massive employment barrier related to mobility loss in urban areas is the ongoing struggle to utilize public transportation during daily commuting. While technological advances in data science and artificial intelligence continue to rapidly spawn new digital tools and electronic solutions, many of these new devices are incomplete answers that leave end users cognitively overwhelmed between device switching and task completion. This project will support foundational research needed to study low-vision behavior and develop more powerful wearables that can handle data-intensive processing, enabling parallel functionality. The project will afford VIS4ION, a revolutionary wearable platform that uses backpack-mounted sensors, advanced machine vision, wireless communications, and human-machine interfaces, the ability to perform ‘connected’ dynamic localization and navigation assistance for the visually impaired in complex urban environments. The project will lead to a healthier low vision population with more gainful employment, a framework for behavioral investigation in disability studies, guidelines for the design and delivery of navigation-focused wearables, students who are well-versed in multi-disciplinary and disability-focused research, and a blueprint for a smart and connected community that enhances economic vitality, safety, security, health and wellbeing, and overall quality of life.
This research will respond to the commuting challenges that stymie employment by creating new connections between visually impaired residents and their surrounding environment through innovations in science and engineering. The project envisions fundamental research in novel behavioral studies, optimized data streams, enhanced power management, and city-agency cooperation to fill a number of knowledge gaps, including (1) lack of a scientifically-principled, experimentally-based understanding of users’ needs and behavioral patterns, (2) limited ability to interpret real-world scenes through computer vision algorithms and excessive computational burden, (3) incomplete understanding of technical methods to balance local versus cloud analytics and manage power effectively, and (4) lack of hypothesis-driven studies with ecological validity that could scale-up to solve commuting challenges. A convergent research plan will fill these gaps and contribute key advancements in computer vision, machine learning, video compression, wireless transmission, human factors, and spatial positioning. By quantifying user needs regarding transportation and subsequently enhancing the safety profile of visually impaired travelers, gainful employment will improve, with secondary impacts on reducing health and economic burden.
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Performance PeriodOctober 2020 - September 2024
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New York University
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Award Number1952180
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Lead PIJohn Ross Rizzo
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Co-PIYao Wang
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Co-PIYi Fang
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Co-PISundeep Rangan
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Co-PIMaurizio Porfiri
Project Material
- Macro-Fiber Composite-Based Tactors for Haptic Applications
- Scalable Feature Compression for Edge-Assisted Object Detection Over Time-Varying Networks
- Commute Booster: A Mobile Application for First/Last Mile and Middle Mile Navigation Support for People With Blindness and Low Vision
- Multi-Cell Multi-Beam Prediction Using Auto-Encoder LSTM for mmWave Systems
- UNav: An Infrastructure-Independent Vision-Based Navigation System for People with Blindness and Low Vision
- Real-Time Loosely Coupled 3DMA GNSS/Doppler Measurements Integration Using a Graph Optimization and Its Performance Assessments in Urban Canyons of New York
- Feature Compression for Rate Constrained Object Detection on the Edge
- Generative Neural Network Channel Modeling for Millimeter-Wave UAV Communication
- What Will the Future of UAV Cellular Communications Be? A Flight from 5G to 6G
- A virtual reality platform to simulate orientation and mobility training for the visually impaired
- Parametrization of High-Rank Line-of-Sight MIMO Channels with Reflected Paths
- Wide-Aperture MIMO via Reflection off a Smooth Surface
- Millimeter Wave Wireless Assisted Robot Navigation With Link State Classification
- Network-Aware 5G Edge Computing for Object Detection: Augmenting Wearables to “See” More, Farther and Faster
- 135GHz CMOS / LTCC MIMO Receiver Array Tile Modules
- Understanding Energy Efficiency and Interference Tolerance in Millimeter Wave Receivers
- An Inconspicuous, Integrated Electronic Travel Aid for Visual Impairment
- Power-Efficient Beam Tracking During Connected Mode DRX in mmWave and Sub-THz Systems
- Drone Detection and Classification Based on Radar Cross Section Signatures
- On Single-User Interactive Beam Alignment in Next Generation Systems: A Deep Learning Viewpoint
- Towards Energy Efficient Mobile Wireless Receivers Above 100 GHz
- Millimeter Wave Channel Modeling via Generative Neural Networks
- LSTM-Based Multi-Link Prediction for mmWave and Sub-THz Wireless Systems
- Capacity Bounds for Communication Systems with Quantization and Spectral Constraints
- Beamformed mmWave System Propagation at 60 GHz in an Office Environment
- Multi-Array Designs for mmWave and Sub-THz Communication to UAVs
- Toward 6G Networks: Use Cases and Technologies
- Analyzing Radar Cross Section Signatures of Diverse Drone Models at mmWave Frequencies
- A Case for Digital Beamforming at mmWave
- Guest Editorial Millimeter-Wave Networking
John-Ross (JR) Rizzo, M.D., M.S.C.I., is a physician-scientist at NYU Langone Health’s Rusk Rehabilitation, where he serves as Vice Chair of Equity and Innovation, Department of Physical Medicine and Rehabilitation with cross-appointments in the Department of Neurology and the Departments of Biomedical & Mechanical and Aerospace Engineering (NYU-Tandon School of Engineering). He is also the Associate Director of Healthcare for the renowned NYU Wireless Laboratory in the Department of Electrical and Computer Engineering at NYU-Tandon. He leads the Visuomotor Integration Laboratory (VMIL), where his team focuses on eye-hand coordination, as it relates to acquired brain injury (ABI), and the REACTIV Laboratory (Rehabilitation Engineering Alliance and Center Transforming Low Vision), where his team focuses on advanced wearables for the sensory deprived and benefits from his own personal experiences with vision loss. He is also the Founder and Chief Medical Advisor of Tactile Navigation Tools, LLC, where he and his team work incessantly to disrupt the assistive technology space for those with visual impairments of all kinds, enhancing human capabilities.