Sight-to-Sound Human-Machine Interface for Guiding and Navigating Visually Impaired People

Yang, Guojun; Saniie, Jafar

doi:10.1109/access.2020.3029426

Cited by 13 publications

(5 citation statements)

References 53 publications

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“…A Support Vector Machine (SVM) classifier with Radial Basis Function (RBF) kernel was used for classification. Another machine vision guidance system that allows visually impaired individuals to navigate their surroundings instantaneously and intuitively was proposed [ 25 ]. The system extracts visual cues from the surrounding environment using a camera and converts them into binaural acoustic cues for users to create cognitive maps.…”

Section: Related Workmentioning

confidence: 99%

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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%

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

View full text Add to dashboard Cite

show abstract

“…In the same way, the smart devices aim to improve the life of Visually Impaired Peoples. 1–3 The main challenging every visually impaired person face is navigation and obstacle detection. With the advancements of IoT, now it becomes easier through many sensors like ultrasonic, accelerometer etc.…”

Section: Introductionmentioning

confidence: 99%

Multi-obstacle aware smart navigation system for visually impaired people in fog connected IoT-cloud environment

Mueen

Awedh

Zafar

2022

Health Informatics J

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Design of smart navigation for visually impaired/blind people is a hindering task. Existing researchers analyzed it in either indoor or outdoor environment and also it’s failed to focus on optimum route selection, latency minimization and multi-obstacle presence. In order to overcome these challenges and to provide precise assistance to visually impaired people, this paper proposes smart navigation system for visually impaired people based on both image and sensor outputs of the smart wearable. The proposed approach involves the upcoming processes: (i) the input query of the visually impaired people (users) is improved by the query processor in order to achieve accurate assistance. (ii) The safest route from source to destination is provided by implementing Environment aware Bald Eagle Search Optimization algorithm in which multiple routes are identified and classified into three different classes from which the safest route is suggested to the users. (iii) The concept of fog computing is leveraged and the optimal fog node is selected in order to minimize the latency. The fog node selection is executed by using Nearest Grey Absolute Decision Making Algorithm based on multiple parameters. (iv) The retrieval of relevant information is performed by means of computing Euclidean distance between the reference and database information. (v) The multi-obstacle detection is carried out by YOLOv3 Tiny in which both the static and dynamic obstacles are classified into small, medium and large obstacles. (vi) The decision upon navigation is provided by implementing Adaptive Asynchronous Advantage Actor-Critic (A3C) algorithm based on fusion of both image and sensor outputs. (vii) Management of heterogeneous is carried out by predicting and pruning the fault data in the sensor output by minimum distance based extended kalman filter for better accuracy and clustering the similar information by implementing Spatial-Temporal Optics Clustering Algorithm to reduce complexity. The proposed model is implemented in NS 3.26 and the results proved that it outperforms other existing works in terms of obstacle detection and task completion time.

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“…Therefore, quickly reaching the correct object can be a problem. Therefore, in the field of RFID systems, the problem of mapping the space in which transponders are located is an important research area [12], [13], [14], [15], [16], [17], [18]. It has also been noted that it is possible to use this technique in a different way and attempt to determine the position of a mobile robot equipped with an RFID reader based on the readings and analysis of the signals obtained when reading information from tags placed at known locations [19], [20], [21], [22].…”

Section: Introductionmentioning

confidence: 99%

Room mapping system using RFID and mobile robots

Skoczylas¹,

Gotówko²,

Salach³

et al. 2022

Annals of Computer Science and Information Systems

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The article presents a prototype indoor space mapping solution using RFID transponders. The autonomous robot reads the information they contain using a set of several readers, which improves the process. The design of the robot prototype is based on the STM32 NUCLEO module. Two types of transponder grids are considered, square and triangular. Simulation results for both grid types show the efficiency of reading information from transponders by the moving robot.

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Sight-to-Sound Human-Machine Interface for Guiding and Navigating Visually Impaired People

Cited by 13 publications

References 53 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

Multi-obstacle aware smart navigation system for visually impaired people in fog connected IoT-cloud environment

Room mapping system using RFID and mobile robots

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