Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

LidSonic V2.0: LiDAR and Deep Learning-based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired

Version 1 : Received: 11 August 2022 / Approved: 11 August 2022 / Online: 11 August 2022 (11:12:58 CEST)

How to cite: Busaeed, S.; Katib, I.; Albeshri, A.; Corchado, J.M.; Yigitcanlar, T.; Mehmood, R. LidSonic V2.0: LiDAR and Deep Learning-based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired. Preprints 2022, 2022080215 (doi: 10.20944/preprints202208.0215.v1). Busaeed, S.; Katib, I.; Albeshri, A.; Corchado, J.M.; Yigitcanlar, T.; Mehmood, R. LidSonic V2.0: LiDAR and Deep Learning-based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired. Preprints 2022, 2022080215 (doi: 10.20944/preprints202208.0215.v1).

Abstract

Over a billion people around the world are disabled, among them, 253 million are visually impaired or blind, and this number is greatly increasing due to ageing, chronic diseases, poor environment, and health. Despite many proposals, the current devices and systems lack maturity and do not completely fulfill user requirements and satisfaction. Increased research activity in this field is required to encourage the development, commercialization, and widespread acceptance of low-cost and affordable assistive technologies for visual impairment and other disabilities. This paper proposes a novel approach using a LiDAR with a servo motor and an ultrasonic sensor to collect data and predict objects using deep learning for environment perception and navigation. We adopted this approach in a pair of smart glasses, called LidSonic V2.0, to enable the identification of obstacles for the visually impaired. The LidSonic system consists of an Arduino Uno edge computing device integrated into the smart glasses and a smartphone app that transmits data via Bluetooth. Arduino gathers data, operates the sensors on smart glasses, detects obstacles using simple data processing, and provides buzzer feedback to visually impaired users. The smartphone application collects data from Arduino, detects and classifies items in the spatial environment, and gives spoken feedback to the user on the detected objects. In comparison to image processing-based glasses, LidSonic uses far less processing time and energy to classify obstacles using simple LiDAR data, according to several integer measurements. We comprehensively describe the proposed system's hardware and software design, construct their prototype implementations, and test them in real-world environments. Using the open platforms, WEKA and TensorFlow, the entire LidSonic system is built with affordable off-the-shelf sensors and a microcontroller board costing less than $80. Essentially, we provide designs of an inexpensive, miniature, green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. Our approach affords faster inference and decision-making using relatively low energy with smaller data sizes as well as faster communications for the edge, fog, and cloud computing.

Keywords

visually impaired; smart mobility; sensors; LiDAR; ultrasonic; deep learning; obstacle detection; obstacle recognition; assistive tools; edge computing; green computing; sustainability; Arduino Uno; Smart App

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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