Deep Neural Network-Based Modeling of Multimodal Human–Computer Interaction in Aircraft Cockpits
Salvato in:
| Pubblicato in: | Future Internet vol. 17, no. 3 (2025), p. 127 |
|---|---|
| Autore principale: | |
| Altri autori: | , |
| Pubblicazione: |
MDPI AG
|
| Soggetti: | |
| Accesso online: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Tags: |
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3181453752 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1999-5903 | ||
| 024 | 7 | |a 10.3390/fi17030127 |2 doi | |
| 035 | |a 3181453752 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231464 |2 nlm | ||
| 100 | 1 | |a Wang, Li |u School of Electronic & Electrical Engineering, Baoji University of Arts and Sciences, Baoji 721016, China | |
| 245 | 1 | |a Deep Neural Network-Based Modeling of Multimodal Human–Computer Interaction in Aircraft Cockpits | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Improving the performance of human–computer interaction systems is an essential indicator of aircraft intelligence. To address the limitations of single-modal interaction methods, a multimodal interaction model based on gaze and EEG target selection is proposed using deep learning technology. This model consists of two parts: target classification and intention recognition. The target classification model based on long short-term memory networks is established and trained by combining the eye movement information of the operator. The intention recognition model based on transformers is constructed and trained by combining the operator’s EEG information. In the application scenario of the aircraft radar page system, the highest accuracy of the target classification model is 98%. The intention recognition rate obtained by training the 32-channel EEG information in the intention recognition model is 98.5%, which is higher than other compared models. In addition, we validated the model on a simulated flight platform, and the experimental results show that the proposed multimodal interaction framework outperforms the single gaze interaction in terms of performance. | |
| 653 | |a Aircraft | ||
| 653 | |a Software | ||
| 653 | |a Eye movements | ||
| 653 | |a Accuracy | ||
| 653 | |a Classification | ||
| 653 | |a Recognition | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Aviation | ||
| 653 | |a Aircraft performance | ||
| 653 | |a Interaction models | ||
| 653 | |a Methods | ||
| 653 | |a Machine learning | ||
| 653 | |a Speech | ||
| 700 | 1 | |a Zhang, Heming |u School of Optoelectronic Engineering, Xi’an Technological University, Xi’an 710000, China; <email>xatu_zhangheming@163.com</email> | |
| 700 | 1 | |a Wang, Changyuan |u School of Computer Science, Xi’an Technological University, Xi’an 710021, China; <email>cyw901@163.com</email> | |
| 773 | 0 | |t Future Internet |g vol. 17, no. 3 (2025), p. 127 | |
| 786 | 0 | |d ProQuest |t ABI/INFORM Global | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3181453752/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3181453752/fulltextwithgraphics/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3181453752/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |