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ElectraSight: Smart Glasses with Fully Onboard Non-Invasive Eye Tracking Using Hybrid Contact and Contactless EOG

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arxiv 2412.14848 v1 pith:2Y5KPXZC submitted 2024-12-19 eess.SP

classification eess.SP
keywords trackingelectrasightglassesmovementsmartsystemsaccuracybattery
verification ladder T0 review T1 audit T2 compute T3 formal
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Smart glasses with integrated eye tracking technology are revolutionizing diverse fields, from immersive augmented reality experiences to cutting-edge health monitoring solutions. However, traditional eye tracking systems rely heavily on cameras and significant computational power, leading to high-energy demand and privacy issues. Alternatively, systems based on electrooculography (EOG) provide superior battery life but are less accurate and primarily effective for detecting blinks, while being highly invasive. The paper introduces ElectraSight, a non-invasive plug-and-play low-power eye tracking system for smart glasses. The hardware-software co-design of the system is detailed, along with the integration of a hybrid EOG (hEOG) solution that incorporates both contact and contactless electrodes. Within 79 kB of memory, the proposed tinyML model performs real-time eye movement classification with 81% accuracy for 10 classes and 92% for 6 classes, not requiring any calibration or user-specific fine-tuning. Experimental results demonstrate that ElectraSight delivers high accuracy in eye movement and blink classification, with minimal overall movement detection latency (90% within 60 ms) and an ultra-low computing time (301 {\mu}s). The power consumption settles down to 7.75 mW for continuous data acquisition and 46 mJ for the tinyML inference. This efficiency enables continuous operation for over 3 days on a compact 175 mAh battery. This work opens new possibilities for eye tracking in commercial applications, offering an unobtrusive solution that enables advancements in user interfaces, health diagnostics, and hands-free control systems.

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Cited by 1 Pith paper

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  1. VergeIO: Depth-Aware Eye Interaction on Glasses

    cs.HC 2025-07 conditional novelty 6.0 of 10

    With dry electrodes on a glasses frame, VergeIO classifies vergence shifts among 30, 70, and 200 cm at 82.7% accuracy for six gestures and up to 97.4% for a four-gesture subset, without per-user calibration.

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