5G Edge Vision: Wearable Assistive Technology for People with Blindness and Low Vision

TL;DR

This paper presents an adaptive 5G edge streaming platform that enhances wearable assistive technology for visually impaired individuals by enabling real-time, robust AI microservices despite wireless network variability.

Contribution

It introduces a novel adaptive streaming platform for 5G edge networks that improves real-time AI microservice support for visually impaired users.

Findings

Robust performance under varying network loads

Effective real-time video offloading to edge servers

Enhanced support for assistive microservices

Abstract

In an increasingly visual world, people with blindness and low vision (pBLV) face substantial challenges in navigating their surroundings and interpreting visual information. From our previous work, VIS4ION is a smart wearable that helps pBLV in their day-to-day challenges. It enables multiple artificial intelligence (AI)-based microservices such as visual scene processing, navigation, and vision-language inference. These microservices require powerful computational resources and, in some cases, stringent inference times, hence the need to offload computation to edge servers. This paper introduces a novel video streaming platform that improves the capabilities of VIS4ION by providing real-time support of the microservices at the network edge. When video is offloaded wirelessly to the edge, the time-varying nature of the wireless network requires the use of adaptation strategies for a seamless video service. We demonstrate the performance of an adaptive real-time video streaming platform through experimentation with an open-source 5G deployment based on open air interface (OAI). The experiments demonstrate the ability to provide the microservices robustly in time-varying network loads.