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

{"title":"Eye-gesture control of computer systems via artificial intelligence.","authors":"Nachaat Mohamed","doi":"10.12688/f1000research.144962.3","DOIUrl":null,"url":null,"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.","PeriodicalId":12260,"journal":{"name":"F1000Research","volume":"13 ","pages":"109"},"PeriodicalIF":0.0000,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11876798/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"F1000Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.12688/f1000research.144962.3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"Pharmacology, Toxicology and Pharmaceutics","Score":null,"Total":0}

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.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

求助全文

约1分钟内获得全文去求助

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

期刊介绍： F1000Research publishes articles and other research outputs reporting basic scientific, scholarly, translational and clinical research across the physical and life sciences, engineering, medicine, social sciences and humanities. F1000Research is a scholarly publication platform set up for the scientific, scholarly and medical research community; each article has at least one author who is a qualified researcher, scholar or clinician actively working in their speciality and who has made a key contribution to the article. Articles must be original (not duplications). All research is suitable irrespective of the perceived level of interest or novelty; we welcome confirmatory and negative results, as well as null studies. F1000Research publishes different type of research, including clinical trials, systematic reviews, software tools, method articles, and many others. Reviews and Opinion articles providing a balanced and comprehensive overview of the latest discoveries in a particular field, or presenting a personal perspective on recent developments, are also welcome. See the full list of article types we accept for more information.