Evidence map›Paper›PMID 40150717›Full record

ArticleBioengineering (Basel, Switzerland)2025

A Novel Tactile Learning Assistive Tool for the Visually and Hearing Impaired with 3D-CNN and Bidirectional LSTM Leveraging Morse Code Technology.

Shabana Ziyad Puthu Vedu, May Altulyan, Pradeep Kumar Singh

Abstract read
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Advances in AI Technology in Healthcare.Bioengineering (Basel, Switzerland) · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Shabana Ziyad Puthu VeduDepartment of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Kharj 11942, Saudi Arabia.
May AltulyanDepartment of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Kharj 11942, Saudi Arabia.
Pradeep Kumar SinghDepartment of Computer Science and Engineering, Central University of Jammu, Jammu and Kashmir 181 143, India.ORCID 0000-0002-7676-9014

Funding

Prince Sattam bin Abdulaziz University PSAU/2024/01/29707
6 · The paper itself

Abstract

Educating deafblind children is a highly specialized field that requires computer-assisted learning tools to address the challenges of auditory and visual impairments. The objective is to reduce their difficulties in communication with their peers and to empower them to learn independently in a classroom environment. Braille and assistive tools have become profoundly beneficial for deafblind children, serving as an essential means of communication and knowledge acquisition, enabling them to live independently. This study aims to develop an assistive tool that bridges the limitations of conventional tactile methodologies by incorporating the latest artificial intelligence techniques, enabling children to learn with greater ease. The research leverages Morse code technology to facilitate communication with deafblind children. The speaker's lip movements are converted into text using the deep learning techniques of a 3D convolutional neural network and a bidirectional long short-term memory neural network. Experimental evaluations of this text conversion model show a word error rate of 2% and an accuracy rate of 98%. The text is then converted into Morse code and communicated to the deafblind child through a wearable device. The significance of this assistive tool lies in its discreet design, resembling a smartwatch. Adolescents can wear the proposed wearable device confidently without feeling self-conscious or embarrassed.

Indexed as

3D convolutional neural networkhearing and visually impairedhuman–computer interactionlearning assistive toolLSTMmorse codetactile learningword error rate

Identifiers

PMID40150717
PMCPMC11939283

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.