The architectural design of smart blind assistant using IoT with deep learning paradigm

Rahman, Wahidur; Tashfia, Saima Siddique; Islam, Rahabul; Hasan, Md. Mahmodul; Sultan, Sadee Ibn; Mia, Shisir; Rahman, Mohammad Motiur

doi:10.1016/j.iot.2020.100344

Cited by 40 publications

(15 citation statements)

References 7 publications

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“…M.W. Rahman et al presented an IoT-based blind stick system to guide visually impaired individuals [ 30 ]. The IoT blind stick functions using a microcontroller that has three US sensors to detect incoming collisions, an accelerator to detect sudden changes in motion, and a camera to capture surrounding obstacle images to inform the caretaker over the cloud.…”

Section: Related Workmentioning

confidence: 99%

An IoT Machine Learning-Based Mobile Sensors Unit for Visually Impaired People

Dhou

Nabulsi

Al-Ali

et al. 2022

Sensors

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Visually impaired people face many challenges that limit their ability to perform daily tasks and interact with the surrounding world. Navigating around places is one of the biggest challenges that face visually impaired people, especially those with complete loss of vision. As the Internet of Things (IoT) concept starts to play a major role in smart cities applications, visually impaired people can be one of the benefitted clients. In this paper, we propose a smart IoT-based mobile sensors unit that can be attached to an off-the-shelf cane, hereafter a smart cane, to facilitate independent movement for visually impaired people. The proposed mobile sensors unit consists of a six-axis accelerometer/gyro, ultrasonic sensors, GPS sensor, cameras, a digital motion processor and a single credit-card-sized single-board microcomputer. The unit is used to collect information about the cane user and the surrounding obstacles while on the move. An embedded machine learning algorithm is developed and stored in the microcomputer memory to identify the detected obstacles and alarm the user about their nature. In addition, in case of emergencies such as a cane fall, the unit alerts the cane user and their guardian. Moreover, a mobile application is developed to be used by the guardian to track the cane user via Google Maps using a mobile handset to ensure safety. To validate the system, a prototype was developed and tested.

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Section: Related Workmentioning

confidence: 99%

An IoT Machine Learning-Based Mobile Sensors Unit for Visually Impaired People

Dhou

Nabulsi

Al-Ali

et al. 2022

Sensors

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“…e model uses the classification of the fundus picture of DR into five classes to determine the patient's stage of DR. Additionally, the CNNs model was enhanced for greater accuracy with the use of transfer learning and hyper-parameter tuning, which was not possible when using nontransferred learning for noisy data. Images have been preprocessed using normalization and data augmentation techniques, and noise has been removed using nonlocal means denoising (NLMD) techniques [8][9][10].…”

Section: Review Of Literaturementioning

confidence: 99%

“…One of the most common chronic diseases in the world, diabetes develops when the body is unable to properly use or manufacture the hormone insulin. According to the World Health Organization (WHO), diabetes was a factor in more than 1.6 million fatalities in 2016 [5][6][7][8][9][10]. Blood glucose levels in diabetic people frequently create high levels, which can harm and fail the body's organs.…”

Section: Introductionmentioning

confidence: 99%

Minimized Computations of Deep Learning Technique for Early Diagnosis of Diabetic Retinopathy Using IoT-Based Medical Devices

Ayoub

Khan

Jadhav³

et al. 2022

Computational Intelligence and Neuroscience

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Diabetes mellitus is the main cause of diabetic retinopathy, the most common cause of blindness worldwide. In order to slow down or prevent vision loss and degeneration, early detection and treatment are essential. For the purpose of detecting and classifying diabetic retinopathy on fundus retina images, numerous artificial intelligence-based algorithms have been put forth by the scientific community. Due to its real-time relevance to everyone’s lives, smart healthcare is attracting a lot of interest. With the convergence of IoT, this attention has increased. The leading cause of blindness among persons in their working years is diabetic eye disease. Millions of people live in the most populous Asian nations, including China and India, and the number of diabetics among them is on the rise. To provide medical screening and diagnosis for this rising population of diabetes patients, skilled clinicians faced significant challenges. Our objective is to use deep learning techniques to automatically detect blind spots in eyes and determine how serious they may be. We suggest an enhanced convolutional neural network (ECNN) utilizing a genetic algorithm in this paper. The ECNN technique’s accuracy results are compared to those of existing approaches like the K-nearest neighbor approach, convolutional neural network, and support vector machine with the genetic algorithm.

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“…Waheed-Rehman et al [11] present smart assistive design for blind peoples The model suggested by R. Bhavani, and S. Ananthakumaran [12] tried to assist the blind peoples with a navigation and directional aid, it also provides lane notification and an understanding of the environment. It designed with highly sensitive sensors.…”

Section: Related Workmentioning

confidence: 99%

Mobile Based IoT Solution for Helping Visual Impairment Users

Abdel-Jaber¹,

Albazar²,

Abdel‐Wahab³

et al. 2021

AIT

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Blind people have many tasks to do in their lives. However, blindness generates challenges for them to perform their tasks. Many blind persons use a traditional stick to move around and to perform their tasks. But the obstacles are not detected in traditional stick, it is ineffective for visually impaired people. The blind person has no idea what kind of objects or obstacles are in front of him/her. The blind person has no idea what size the object is or how far away he or she is from it. It is difficult for a blind person to get around. To assist people with vision impairment by making many of their daily tasks simple, comfortable, and organized, they will be able to recognize anything (an obstacle for blind people). A smart stick with mobile application can be used. One of the solutions is a mobile-based Internet of Things solution, which is a stick intended to assist visually impaired people to navigate more easily. It enables blindness and low vision people to navigate and carry out their daily tasks with ease and comfort. A technologically advanced blind stick that enables visually impaired people to move with ease. This paper proposes a system of software and hardware that helps visually impairment people to find their ways in an easy and comfortable way. The proposed system uses smart stick and mobile application to help blind and visually impaired people to identify objects (such as walls, tables, vehicles, people, etc.) in their ways, this can enable them to avoid these objects. In addition, as a result, the system will notify the user through sound from the smartphone. Finally, if he/she gets lost, he will be able to send an SMS with his/her GPS location.

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The architectural design of smart blind assistant using IoT with deep learning paradigm

Cited by 40 publications

References 7 publications

An IoT Machine Learning-Based Mobile Sensors Unit for Visually Impaired People

An IoT Machine Learning-Based Mobile Sensors Unit for Visually Impaired People

Minimized Computations of Deep Learning Technique for Early Diagnosis of Diabetic Retinopathy Using IoT-Based Medical Devices

Mobile Based IoT Solution for Helping Visual Impairment Users

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