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

TL;DR

ElectraSight introduces a non-invasive, low-power, hybrid EOG-based eye tracking system for smart glasses that achieves high accuracy, real-time performance, and long battery life without calibration, enabling diverse commercial applications.

Contribution

The paper presents a novel hybrid EOG hardware design combined with a tinyML model for accurate, calibration-free eye tracking in smart glasses, with ultra-low power consumption.

Findings

81% accuracy for 10 eye movement classes

90% detection latency within 60 ms

Operates continuously for over 3 days on a small battery

Abstract

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.