Low-Power Hierarchical Network: Pervasive Eye-Tracking on Smart Eyewear

Abstract

Pervasive eye-tracking technology for eyewear devices represents a major advancement in wearable computing, enabling intuitive interaction and improving accessibility. However, the low-power constraints of these devices present a significant challenge in balancing accuracy with limited computational capacity. This study focuses on developing and evaluating algorithms for a low-power wearable infrared eye-tracking system conceived to work 24/7. The system includes a custom-built prototype that integrates infrared LEDs and photodiodes, strategically positioned on smart eyewear to estimate gaze direction. A humanoid robot, Ami Desktop, was utilized to create a controlled and robust dataset. Two deep learning architectures were investigated: a Multi-Layer Perceptron (MLP) and a tailored Hierarchical Neural Network (HNN). Variants of these models incorporating dimensionality reduction techniques were implemented to optimize performance and efficiency for lowpower microcontrollers. The results demonstrate the superior accuracy and reasonable computational demands of the HNN models, highlighting their potential for continuous, real-time and portable eye-tracking applications.