VergeIO: Depth-Aware Eye Interaction on Glasses
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912886332325888 |
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| author | Zhang, Xiyuxing Vu, Duc Shen, Chengyi Wang, Yuntao Shi, Yuanchun Chan, Justin |
| author_facet | Zhang, Xiyuxing Vu, Duc Shen, Chengyi Wang, Yuntao Shi, Yuanchun Chan, Justin |
| contents | There is growing industry interest in creating unobtrusive designs for electrooculography (EOG) sensing of eye gestures on glasses (e.g. JINS MEME and Apple eyewear). We present VergeIO, the first EOG-based glasses that enables depth-aware eye interaction using vergence with an optimized electrode layout and novel smart glass prototype. It can distinguish between four and six depth-based eye gestures with 83-98% accuracy using personalized models in a user study across 20 users and 2,400 gesture instances. It generalizes to unseen users with an accuracy of 77-97% without any calibration. To reduce false detections, we incorporate a motion artifact detection pipeline and a preamble-based activation scheme. The system uses dry sensors without any adhesives or gel, and operates in real time with 3 mW power consumption by the sensing front-end, making it suitable for always-on sensing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_02187 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VergeIO: Depth-Aware Eye Interaction on Glasses Zhang, Xiyuxing Vu, Duc Shen, Chengyi Wang, Yuntao Shi, Yuanchun Chan, Justin Human-Computer Interaction There is growing industry interest in creating unobtrusive designs for electrooculography (EOG) sensing of eye gestures on glasses (e.g. JINS MEME and Apple eyewear). We present VergeIO, the first EOG-based glasses that enables depth-aware eye interaction using vergence with an optimized electrode layout and novel smart glass prototype. It can distinguish between four and six depth-based eye gestures with 83-98% accuracy using personalized models in a user study across 20 users and 2,400 gesture instances. It generalizes to unseen users with an accuracy of 77-97% without any calibration. To reduce false detections, we incorporate a motion artifact detection pipeline and a preamble-based activation scheme. The system uses dry sensors without any adhesives or gel, and operates in real time with 3 mW power consumption by the sensing front-end, making it suitable for always-on sensing. |
| title | VergeIO: Depth-Aware Eye Interaction on Glasses |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2507.02187 |