Virtual Keyboard Using Eye-Blinking

The advancement of assistive technologies has significantly transformed the landscape of human-computer interaction, particularly benefiting individuals with physical disabilities who face challenges in using conventional input devices. Among these innovative solutions, the virtual keyboard controlled by eye blinking has emerged as a crucial tool, providing a practical and efficient means of communication and control for individuals with severe motor impairments. Eye-blink-based virtual keyboards typically function through the detection of intentional eye blinks, which are distinguished from natural, involuntary blinks to reduce false inputs. Recent advancements have incorporated machine learning models, such as convolutional neural networks (CNNs), to enhance the accuracy of blink detection and adapt to individual blinking patterns.

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Publication Details

Journal
International Journal for Research in Applied Science and Engineering Technology
Published
2025-04-15
DOI
https://doi.org/10.22214/ijraset.2025.68750
Citations
1
Primary Topic
Gaze Tracking and Assistive Technology
Type
article
Field-Weighted Citation Impact
1.94
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article

Virtual Keyboard Using Eye-Blinking

Prof. Sai Takawale
1 citations
International Journal for Research in Applied Science and Engineering Technology
Gaze Tracking and Assistive Technology
1.94
article

Virtual Keyboard Using Eye-Blinking

Prof. Sai Takawale
article en
1 citations

Abstract

The advancement of assistive technologies has significantly transformed the landscape of human-computer interaction, particularly benefiting individuals with physical disabilities who face challenges in using conventional input devices. Among these innovative solutions, the virtual keyboard controlled by eye blinking has emerged as a crucial tool, providing a practical and efficient means of communication and control for individuals with severe motor impairments. Eye-blink-based virtual keyboards typically function through the detection of intentional eye blinks, which are distinguished from natural, involuntary blinks to reduce false inputs. Recent advancements have incorporated machine learning models, such as convolutional neural networks (CNNs), to enhance the accuracy of blink detection and adapt to individual blinking patterns.

International Journal for Research in Applied Science and Engineering TechnologyVol. 13(4)
Tatyasaheb Kore Dental College and Research Centre (IN)
Openalex Percentile: Top 18%
Gaze Tracking and Assistive Technology
1.94
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