Eye-gesture control of computer systems via artificial intelligence.

Q2 Pharmacology, Toxicology and Pharmaceutics

F1000Research Pub Date : 2025-02-25 eCollection Date: 2024-01-01 DOI:10.12688/f1000research.144962.3

Nachaat Mohamed

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引用次数: 0

Abstract

Background: Artificial Intelligence (AI) offers transformative potential for human-computer interaction, particularly through eye-gesture recognition, enabling intuitive control for users and accessibility for individuals with physical impairments.

Methods: We developed an AI-driven eye-gesture recognition system using tools like OpenCV, MediaPipe, and PyAutoGUI to translate eye movements into commands. The system was trained on a dataset of 20,000 gestures from 100 diverse volunteers, representing various demographics, and tested under different conditions, including varying lighting and eyewear.

Results: The system achieved 99.63% accuracy in recognizing gestures, with slight reductions to 98.9% under reflective glasses. These results demonstrate its robustness and adaptability across scenarios, confirming its generalizability.

Conclusions: This system advances AI-driven interaction by enhancing accessibility and unlocking applications in critical fields like military and rescue operations. Future work will validate the system using publicly available datasets to further strengthen its impact and usability.

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通过人工智能控制计算机系统的眼手势。

背景：人工智能（AI）为人机交互提供了变革性的潜力，特别是通过眼睛手势识别，为用户提供直观的控制，并为身体障碍的个人提供可访问性。方法：利用OpenCV、MediaPipe、PyAutoGUI等工具开发人工智能驱动的眼动识别系统，将眼动转换为指令。该系统接受了来自100名不同志愿者的2万个手势数据集的训练，这些志愿者代表了不同的人口统计数据，并在不同的条件下进行了测试，包括不同的照明和眼镜。结果：该系统识别手势的准确率达到了99.63%，在佩戴反光眼镜的情况下，准确率略降至98.9%。这些结果证明了它的鲁棒性和跨场景适应性，证实了它的泛化性。结论：该系统通过增强军事和救援行动等关键领域的可访问性和解锁应用，推进了人工智能驱动的交互。未来的工作将使用公开可用的数据集验证该系统，以进一步加强其影响和可用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

F1000Research Pharmacology, Toxicology and Pharmaceutics-Pharmacology, Toxicology and Pharmaceutics (all)

CiteScore

5.00

自引率

0.00%

发文量

1646

审稿时长

1 weeks

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