Evaluating Electric Charge Variation Sensors for Camera-free Eye Tracking on Smart Glasses

TL;DR

This study evaluates the real-world performance of a contactless electrooculography-based eye-tracking system for smart glasses, highlighting its potential and challenges in everyday environments with user and environmental variability.

Contribution

It provides the first large-scale field assessment of QVar-based eye tracking, revealing its accuracy range and environmental robustness in practical scenarios.

Findings

Classification accuracy ranges from 57% to 89%, averaging 74.5%.

Environmental noise significantly degrades system performance.

User variability impacts classification success.

Abstract

Contactless Electrooculography (EOC) using electric charge variation (QVar) sensing has recently emerged as a promising eye-tracking technique for wearable devices. QVar enables low-power and unobtrusive interaction without requiring skin-contact electrodes. Previous work demonstrated that such systems can accurately classify eye movements using onboard TinyML under controlled laboratory conditions. However, the performance and robustness of contactless EOC in real-world scenarios, where environmental noise and user variability are significant, remain largely unexplored. In this paper, we present a field evaluation of a previously proposed QVar-based eye-tracking system, assessing its limitations in everyday usage contexts across 29 users and 100 recordings in everyday scenarios such as working in front of a laptop. Our results show that classification accuracy varies between 57% and 89% depending on the user, with an average of 74.5%, and degrades significantly in the presence of nearby electronic noise sources. These results show that contactless EOC remains viable under realistic conditions, though subject variability and environmental factors can significantly affect classification accuracy. The findings inform the future development of wearable gaze interfaces for human-computer interaction and augmented reality, supporting the transition of this technology from prototype to practice.