LLM-Glasses: GenAI-driven Glasses with Haptic Feedback for Navigation of Visually Impaired People
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915737208094720 |
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| author | Tokmurziyev, Issatay Cabrera, Miguel Altamirano Khan, Muhammad Haris Mahmoud, Yara Tsetserukou, Dzmitry |
| author_facet | Tokmurziyev, Issatay Cabrera, Miguel Altamirano Khan, Muhammad Haris Mahmoud, Yara Tsetserukou, Dzmitry |
| contents | LLM-Glasses is a wearable navigation system which assists visually impaired people by utilizing YOLO-World object detection, GPT-4o-based reasoning, and haptic feedback for real-time guidance. The device translates visual scene understanding into intuitive tactile feedback on the temples, allowing hands-free navigation. Three studies evaluate the system: recognition of 13 haptic patterns with an average recognition rate of 81.3%, VICON-based guidance with predefined paths using haptic cues, and an LLM-guided scene evaluation with decision accuracies of 91.8% without obstacles, 84.6% with static obstacles, and 81.5% with dynamic obstacles. These results show that LLM-Glasses can deliver reliable navigation support in controlled environments and motivate further work on responsiveness and deployment in more complex real-world scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16475 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM-Glasses: GenAI-driven Glasses with Haptic Feedback for Navigation of Visually Impaired People Tokmurziyev, Issatay Cabrera, Miguel Altamirano Khan, Muhammad Haris Mahmoud, Yara Tsetserukou, Dzmitry Human-Computer Interaction Robotics LLM-Glasses is a wearable navigation system which assists visually impaired people by utilizing YOLO-World object detection, GPT-4o-based reasoning, and haptic feedback for real-time guidance. The device translates visual scene understanding into intuitive tactile feedback on the temples, allowing hands-free navigation. Three studies evaluate the system: recognition of 13 haptic patterns with an average recognition rate of 81.3%, VICON-based guidance with predefined paths using haptic cues, and an LLM-guided scene evaluation with decision accuracies of 91.8% without obstacles, 84.6% with static obstacles, and 81.5% with dynamic obstacles. These results show that LLM-Glasses can deliver reliable navigation support in controlled environments and motivate further work on responsiveness and deployment in more complex real-world scenarios. |
| title | LLM-Glasses: GenAI-driven Glasses with Haptic Feedback for Navigation of Visually Impaired People |
| topic | Human-Computer Interaction Robotics |
| url | https://arxiv.org/abs/2503.16475 |