Construction and Analysis of a Novel Wearable Assistive Device for a Visually Impaired Person

Akram, Shahid; Mahmood, Ali Shakir; Ullah, Ihsan; Mujtabah, Muhammad Tahir; Yasin, Ali Bin; Butt, Asif Raza; Shafique, Muhammad; Manzoor, Sajjad

doi:10.1155/2020/6153128

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…). Moreover, performance evaluation for SVM category (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13) ) all achieved the highest TA of (100%), with AUC (1) with TPR = 100% and TNR = 100%.…”

Section: Discussionmentioning

confidence: 99%

“…Recent trends predict a drastic increase in smartphone users, with an expected increase of up to 9 billion by 2021 [2,3]. ere are various applications meant to assist visually impaired users, such as screen readers, sound and speech output devices, location finders, wearable devices for mobility, stereo vision-based systems, and virtual assistants [4][5][6][7]. Rapid growth in smartphone usage has changed the learning attitude of people [8].…”

Section: Introductionmentioning

confidence: 99%

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Detection of Touchscreen-Based Urdu Braille Characters Using Machine Learning Techniques

Shokat

Riaz

Rizvi

et al. 2021

Mobile Information Systems

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Revolution in technology is changing the way visually impaired people read and write Braille easily. Learning Braille in its native language can be more convenient for its users. This study proposes an improved backend processing algorithm for an earlier developed touchscreen-based Braille text entry application. This application is used to collect Urdu Braille data, which is then converted to Urdu text. Braille to text conversion has been done on Hindi, Arabic, Bangla, Chinese, English, and other languages. For this study, Urdu Braille Grade 1 data were collected with multiclass (39 characters of Urdu represented by class 1, Alif (ﺍ), to class 39, Bri Yay (ے). Total (N = 144) cases for each class were collected. The dataset was collected from visually impaired students from The National Special Education School. Visually impaired users entered the Urdu Braille alphabets using touchscreen devices. The final dataset contained (N = 5638) cases. Reconstruction Independent Component Analysis (RICA)-based feature extraction model is created for Braille to Urdu text classification. The multiclass was categorized into three groups (13 each), i.e., category-1 (1–13), Alif-Zaal (ﺫ - ﺍ), category-2 (14–26), Ray-Fay (ﻒ - ﺮ), and category-3 (27–39), Kaaf-Bri Yay (ے - ﻕ), to give better vision and understanding. The performance was evaluated in terms of true positive rate, true negative rate, positive predictive value, negative predictive value, false positive rate, total accuracy, and area under the receiver operating curve. Among all the classifiers, support vector machine has achieved the highest performance with a 99.73% accuracy. For comparisons, robust machine learning techniques, such as support vector machine, decision tree, and K-nearest neighbors were used. Currently, this work has been done on only Grade 1 Urdu Braille. In the future, we plan to enhance this work using Grade 2 Urdu Braille with text and speech feedback on touchscreen-based android phones.

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“…). Moreover, performance evaluation for SVM category (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13) ) all achieved the highest TA of (100%), with AUC (1) with TPR = 100% and TNR = 100%.…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of Touchscreen-Based Urdu Braille Characters Using Machine Learning Techniques

Shokat

Riaz

Rizvi

et al. 2021

Mobile Information Systems

View full text Add to dashboard Cite

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“…Some can be placed directly on the white cane [17], and others are held by the user [18]. Some designs have even used belts [19] or shoes [20] to position them. For a review of the most important and recent ones, see the review carried by Messaoudi et al [21].…”

Section: Introductionmentioning

confidence: 99%

Safe Displacements Device for All Conditions Blind People

et al. 2023

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One of the challenges faced by the blind to achieve optimum mobility is obstacles detection and avoidance. The traditional aid is the mobility white cane, but nowadays, there are also electronic travel aids. However, none of them is widely used. The eBAT (electronic Buzzer for Autonomous Travel) has been designed to provide protection and easy usage, interacting with a user’s mobile phone. To improve its performance, a usage test was carried out by 25 totally blind users divided by sex, age range and autonomy in mobility. The main results are that the eBAT gives a reduction in the involuntary contacts but also decreases the walking speed. There are differences between sex, age and mobility groups but with limited statistical significance, and there are also some correlations between variables.

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Designing an embedded multi-sensor autonomous system for walker-assisted locomotion

Jasim,

Lafta

2023

The Fourth Scientific Conference for Electrical Engineering Techniques Research (Eetr2022)

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Construction and Analysis of a Novel Wearable Assistive Device for a Visually Impaired Person

Cited by 6 publications

References 20 publications

Detection of Touchscreen-Based Urdu Braille Characters Using Machine Learning Techniques

Detection of Touchscreen-Based Urdu Braille Characters Using Machine Learning Techniques

Safe Displacements Device for All Conditions Blind People

Designing an embedded multi-sensor autonomous system for walker-assisted locomotion

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