Intelligent Navigation System for the Visually Impaired - A Deep Learning Approach

Yadav, Deepak; Mookherji, Somsankar; Gomes, Joanne; Patil, Siddhant

doi:10.1109/iccmc48092.2020.iccmc-000121

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…Yadav et al [ 89 ] propose a smart navigation system that relentlessly scans the environment, and then detects and classifies neighboring objects using a 4-layered convolutional neural network (CNN), which has been trained on a data set containing 2513 permutations of the various images of household objects that an individual may encounter in daily life. The device has achieved a success rate of serving within a response time of less than 50 ms.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence of Things Applied to Assistive Technology: A Systematic Literature Review

Freitas

Piai

Farias

et al. 2022

Sensors

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According to the World Health Organization, about 15% of the world’s population has some form of disability. Assistive Technology, in this context, contributes directly to the overcoming of difficulties encountered by people with disabilities in their daily lives, allowing them to receive education and become part of the labor market and society in a worthy manner. Assistive Technology has made great advances in its integration with Artificial Intelligence of Things (AIoT) devices. AIoT processes and analyzes the large amount of data generated by Internet of Things (IoT) devices and applies Artificial Intelligence models, specifically, machine learning, to discover patterns for generating insights and assisting in decision making. Based on a systematic literature review, this article aims to identify the machine-learning models used across different research on Artificial Intelligence of Things applied to Assistive Technology. The survey of the topics approached in this article also highlights the context of such research, their application, the IoT devices used, and gaps and opportunities for further development. The survey results show that 50% of the analyzed research address visual impairment, and, for this reason, most of the topics cover issues related to computational vision. Portable devices, wearables, and smartphones constitute the majority of IoT devices. Deep neural networks represent 81% of the machine-learning models applied in the reviewed research.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence of Things Applied to Assistive Technology: A Systematic Literature Review

Freitas

Piai

Farias

et al. 2022

Sensors

View full text Add to dashboard Cite

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Analysis of Navigation Assistants for Blind and Visually Impaired People: A Systematic Review

2021

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Over the last few decades, the development in the field of navigation and routing devices has become a hindering task for the researchers to develop smart and intelligent guiding mechanism at indoor and outdoor locations for blind and visually impaired people (BVIPs). The existing research need to be analysed from a historical perception including early research on the first electronic travel aids to the use of modern artificial vision models for the navigation of BVIPs. Diverse approaches such as: e-cane or guide dog, infrared-based cane, laser based walker and many others are proposed for the navigation of BVIPs. But most of these techniques have limitations such as: infrared and ultrasonic based assistance has short range capacities for object detection. While laser based assistance can harm other people if it directly hit them on their eyes or any other part of the body. These trade-offs are critical to bring this technology in practice.To systematically assess, analyze, and identify the primary studies in this specialized field and provide an overview of the trends and empirical evidence in the proposed field. This systematic research work is performed by defining a set of relevant keywords, formulating four research questions, defining selection criteria for the articles, and synthesizing the empirical evidence in this area. Our pool of studies include 191 most relevant articles to the proposed field reported between 2011 and 2020 (a portion of 2020 is included). This systematic mapping will help the researchers, engineers, and practitioners to make more authentic decisions for finding gaps in the available navigation assistants and suggest a new and enhanced smart assistant application accordingly to ensure safety and accurate guidance of the BVIPs. This research work have several implications in particular the impact of reducing fatalities and major injuries of BVIPs. INDEX TERMS Blind and visually impaired people, healthcare, smart devices, systematic literature review.

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Smart Stick Navigation System for Visually Impaired Based on Machine Learning Algorithms Using Sensors Data

Gharghan,

Kamel,

Marir

et al. 2024

JSAN

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Visually Impaired People (VIP) face significant challenges in their daily lives, relying on others or trained dogs for assistance when navigating outdoors. Researchers have developed the Smart Stick (SS) system as a more effective aid than traditional ones to address these challenges. Developing and utilizing the SS systems for VIP improves mobility, reliability, safety, and accessibility. These systems help users by identifying obstacles and hazards, keeping VIP safe and efficient. This paper presents the design and real-world implementation of an SS using an Arduino Nano microcontroller, GPS, GSM module, heart rate sensor, ultrasonic sensor, moisture sensor, vibration motor, and Buzzer. Based on sensor data, the SS can provide warning signals to VIP about the presence of obstacles and hazards around them. Several Machine Learning (ML) algorithms were used to improve the SS alert decision accuracy. Therefore, this paper used sensor data to train and test ten ML algorithms to find the most effective alert decision accuracy. Based on the ML algorithms, the alert decision, including the presence of obstacles, environmental conditions, and user health conditions, was examined using several performance metrics. Results showed that the AdaBoost, Gradient boosting, and Random Forest ML algorithms outperformed others and achieved an AUC and specificity of 100%, with 99.9% accuracy, F1-score, precision, recall, and MCC in the cross-validation phase. Integrating sensor data with ML algorithms revealed that the SS enables VIP to live independently and move safely without assistance.

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Intelligent Navigation System for the Visually Impaired - A Deep Learning Approach

Cited by 4 publications

References 14 publications

Artificial Intelligence of Things Applied to Assistive Technology: A Systematic Literature Review

Artificial Intelligence of Things Applied to Assistive Technology: A Systematic Literature Review

Analysis of Navigation Assistants for Blind and Visually Impaired People: A Systematic Review

Smart Stick Navigation System for Visually Impaired Based on Machine Learning Algorithms Using Sensors Data

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