Design of eye control human–computer interaction interface based on smooth tracking

A deep learning model based on smooth tracking, the adaptive lightweight eye control tracking Transformer, is proposed to address the accuracy and real-time issues of eye control human–computer interaction interfaces. By utilizing convolutional neural network and recurrent neural network frameworks,...

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Veröffentlicht in:	Discover applied sciences Jg. 7; H. 11; S. 1346 - 17
1. Verfasser:	Ding, Chuanfeng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.11.2025 Springer Nature B.V Springer
Schlagworte:	Accuracy Algorithms Applied and Technical Physics Artificial intelligence Artificial neural networks Chemistry/Food Science CNN Computer applications Computer vision Datasets Deep learning Earth Sciences Efficiency Electrodes Engineering Environment Errors Eye controlled human–computer interaction Eye movements Human-computer interface Inference Interfaces Machine learning Materials Science Neural networks Performance evaluation Real time Recurrent neural networks Research methodology RNN Smooth tracking Success Tracking Usability User experience User needs Virtual reality Deep learning Eye controlled human–computer interaction CNN RNN Smooth tracking
ISSN:	3004-9261, 2523-3963, 3004-9261, 2523-3971
Online-Zugang:	Volltext
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