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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.13203 |
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| _version_ | 1866909650037768192 |
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| author | Wang, Yikan |
| author_facet | Wang, Yikan |
| contents | Wearable devices, such as smartwatches and head-mounted displays, are increasingly used for prolonged tasks like remote learning and work, but sustained interaction often leads to user fatigue, reducing efficiency and engagement. This study proposes a fatigue-aware adaptive interface system for wearable devices that leverages deep learning to analyze physiological data (e.g., heart rate, eye movement) and dynamically adjust interface elements to mitigate cognitive load. The system employs multimodal learning to process physiological and contextual inputs and reinforcement learning to optimize interface features like text size, notification frequency, and visual contrast. Experimental results show a 18% reduction in cognitive load and a 22% improvement in user satisfaction compared to static interfaces, particularly for users engaged in prolonged tasks. This approach enhances accessibility and usability in wearable computing environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_13203 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fatigue-Aware Adaptive Interfaces for Wearable Devices Using Deep Learning Wang, Yikan Machine Learning Wearable devices, such as smartwatches and head-mounted displays, are increasingly used for prolonged tasks like remote learning and work, but sustained interaction often leads to user fatigue, reducing efficiency and engagement. This study proposes a fatigue-aware adaptive interface system for wearable devices that leverages deep learning to analyze physiological data (e.g., heart rate, eye movement) and dynamically adjust interface elements to mitigate cognitive load. The system employs multimodal learning to process physiological and contextual inputs and reinforcement learning to optimize interface features like text size, notification frequency, and visual contrast. Experimental results show a 18% reduction in cognitive load and a 22% improvement in user satisfaction compared to static interfaces, particularly for users engaged in prolonged tasks. This approach enhances accessibility and usability in wearable computing environments. |
| title | Fatigue-Aware Adaptive Interfaces for Wearable Devices Using Deep Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.13203 |