Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929586310217728 |
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| author | Duan, Shiyu Wang, Ziyi Wang, Shixiao Chen, Mengmeng Zhang, Runsheng |
| author_facet | Duan, Shiyu Wang, Ziyi Wang, Shixiao Chen, Mengmeng Zhang, Runsheng |
| contents | In an era where user interaction with technology is ubiquitous, the importance of user interface (UI) design cannot be overstated. A well-designed UI not only enhances usability but also fosters more natural, intuitive, and emotionally engaging experiences, making technology more accessible and impactful in everyday life. This research addresses this growing need by introducing an advanced emotion recognition system to significantly improve the emotional responsiveness of UI. By integrating facial expressions, speech, and textual data through a multi-branch Transformer model, the system interprets complex emotional cues in real-time, enabling UIs to interact more empathetically and effectively with users. Using the public MELD dataset for validation, our model demonstrates substantial improvements in emotion recognition accuracy and F1 scores, outperforming traditional methods. These findings underscore the critical role that sophisticated emotion recognition plays in the evolution of UIs, making technology more attuned to user needs and emotions. This study highlights how enhanced emotional intelligence in UIs is not only about technical innovation but also about fostering deeper, more meaningful connections between users and the digital world, ultimately shaping how people interact with technology in their daily lives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06326 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learning Duan, Shiyu Wang, Ziyi Wang, Shixiao Chen, Mengmeng Zhang, Runsheng Human-Computer Interaction Machine Learning In an era where user interaction with technology is ubiquitous, the importance of user interface (UI) design cannot be overstated. A well-designed UI not only enhances usability but also fosters more natural, intuitive, and emotionally engaging experiences, making technology more accessible and impactful in everyday life. This research addresses this growing need by introducing an advanced emotion recognition system to significantly improve the emotional responsiveness of UI. By integrating facial expressions, speech, and textual data through a multi-branch Transformer model, the system interprets complex emotional cues in real-time, enabling UIs to interact more empathetically and effectively with users. Using the public MELD dataset for validation, our model demonstrates substantial improvements in emotion recognition accuracy and F1 scores, outperforming traditional methods. These findings underscore the critical role that sophisticated emotion recognition plays in the evolution of UIs, making technology more attuned to user needs and emotions. This study highlights how enhanced emotional intelligence in UIs is not only about technical innovation but also about fostering deeper, more meaningful connections between users and the digital world, ultimately shaping how people interact with technology in their daily lives. |
| title | Emotion-Aware Interaction Design in Intelligent User Interface Using Multi-Modal Deep Learning |
| topic | Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2411.06326 |