VisiSense: A Comprehensive IOT-based Assistive Technology System for Enhanced Navigation Support for the Visually Impaired

Abstract

The field of visually impaired assistive technology looks for novel approaches to enhance independence and navigation. In this field, systems have to reliably identify and transmit environmental data in order to facilitate visually impaired users' safe and effective navigation. Developing a sophisticated framework for assistive technology that significantly enhances visually impaired navigation is the goal of this research. Make object detection and environmental awareness more efficient, dependable, and intuitive. This study introduces VISISENSE, "A Comprehensive IoT-Based Assistive Technology System for Enhanced Navigation Support for the Visually Impaired." VISISENSE is an IoT-based system with multiple components that enhances object detection. For primary environmental sensing, a handstick with implant sensors, a visual capture and transmission unit for processing visual data, and edge computing for object detection and classification are used. The system makes use of the R-CNN global computer vision model hosted on a cloud server, Mobinet computer vision models, and Logistic Regression with Iterative Learning. VISISENSE's effectiveness is demonstrated by a performance analysis of its object detection accuracy, processing speed, resource utilization, energy consumption, latency, and false positive rate. In all of these categories, VISISENSE performs better than Smart Stick and Smart Navigation. The data includes the fastest processing time of 17 ms, the most efficient resource utilization of 41%, and object detection accuracy of up to 99% at 2 Mbps load. Across all load conditions, VISISENSE has the lowest false positive rate, energy consumption, and latency. The VISISENSE assistive technology system is developed for the visually impaired. Its excellent object detection and navigation accuracy, speed, and efficiency enhance user experience and hold the potential to increase the independence and quality of life for visually impaired people. This research contributes to a significant advancement in assistive devices-smart, responsive technologies for the visually impaired.