Vis-Assist: Computer Vision and Haptic Feedback-Based Wearable Assistive Device for Visually Impaired

Abstract

Visual impairment affects millions of people worldwide, posing significant challenges in their daily lives and personal safety. While assistive technologies, both wearable and non-wearable, can help mitigate these challenges, wearable devices offer the advantage of hands-free operation. In this context, we present Vis-Assist, a novel wearable visual assistive device capable of detecting and classifying objects, measuring their distances, and providing real-time haptic feedback through a vibration motor array, all using an integrated low-cost computational unit without the need for external servers. Our study distinguishes itself by utilizing haptic feedback to convey object information, allowing visually impaired individuals to discern between 19 different object classes following a brief training period. Haptic feedback offers an alternative to audio that doesn't block hearing and can be used alongside it, serving as a complementary solution. The performance of the developed wearable device was evaluated through two types of experiments with four participants. The results demonstrate that users can identify the location of objects and thereby prevent collisions with obstacles. The experiments conducted demonstrate that users, on average, can locate a predefined object, such as a chair, within a 40 m2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {m}<^>{2}$$\end{document} vacant space in under 94 seconds. Furthermore, users exhibit proficiency in finding objects while navigating around obstacles in the same environment, achieving this task in less than 121 seconds on average. The system developed here has high potential to help the self-navigation of visually impaired people and make their daily lives easier. To facilitate further research in this field, the complete source code for this study has been made publicly available on GitHub.