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

2025-11-11

AI Summary

This work addresses the privacy, power consumption, and robustness limitations of camera-based eye-tracking in smart glasses. We propose a non-invasive electrooculography (EOG) acquisition method leveraging contactless charge-variation (QVar) sensing—eliminating both skin contact and optical components. Integrated with a low-power embedded TinyML model, it enables real-time gaze movement classification. To our knowledge, this is the first large-scale in-the-wild evaluation of QVar-based eye-tracking under natural daily conditions, involving 29 participants and 100 sessions of spontaneous interactions. The system achieves a mean classification accuracy of 74.5%, revealing significant impacts of inter-subject physiological variability and ambient electromagnetic noise. Our study validates the feasibility of QVar sensing for wearable gaze interfaces and establishes a new paradigm for privacy-preserving, ultra-low-power,全天候 (all-day, all-environment) eye-tracking—advancing camera-free gaze interaction toward practical deployment.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Privacy
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

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.

Problem

• Evaluating contactless eye tracking robustness in real-world scenarios
• Assessing performance degradation from environmental noise sources
• Addressing subject variability in electric charge variation sensing

Innovation

• Contactless EOC uses QVar sensing for eye tracking
• System classifies eye movements with onboard TinyML
• Field evaluation assesses robustness in real-world scenarios