This work presents a low-power embedded eye-tracking system based on Photosensor Oculography (PSOG) and evaluates lightweight calibration-driven distance-based classifiers for discrete gaze-zone estimation in smart eyewear.The proposed prototype integrates infrared LEDs and photodiodes within a commercial frame and uses sequential illumination to generate 32-dimensional feature vectors at 1 kHz with minimal computational cost.Distancebased classifiers, including Euclidean, standardized Euclidean, and Mahalanobis metrics, were first evaluated using a humanoid robot with human-like ocular geometry under controlled conditions.The selected methods were then extensively evaluated on a human dataset comprising 29 subjects under varying illumination conditions.Results show that the Mahalanobis distance achieves the highest accuracy, while dimensionality reduction improves robustness under changing lighting.A PCA-Mahalanobis configuration provided the best accuracy-complexity trade-off, while stronger feature reduction combined with Euclidean distance enabled competitive performance at very low computational cost, supporting deployment on both high-performance and embedded smart eyewear platforms.

This work presents a low-power wearable Photosensor Oculography (PSOG) gaze-tracking system for smart glasses, repurposing the MAX86181 optical analog front-end, originally designed for photoplethysmography (PPG) to measure periocular reflectance patterns. Infrared LEDs and photodiodes are arranged around the eyes, using time-multiplexed illumination to generate 32-element feature vectors (4 PDs × 4 LEDs × 2 eyes). The system’s integrated analog/digital ambient-light cancellation preserves signal integrity across indoor to outdoor conditions, as validated on a humanoid robot platform. A lightweight Mahalanobis distance-based classifier achieves ~\(97\%\) gaze-region classification accuracy for both 5-zone and 9-zone partitions in the controlled robotic evaluation at 58.6 μs integration time. Power consumption ranges from 11 mW at 50 fps to 18.5 mW at 150 fps, corresponding to an estimated operating time from approximately 34 hours to 20 hours, respectively, on a 100 mAh, 3.7 V LiPo battery under ideal conversion efficiency. These results demonstrate the feasibility of repurposing a PPG-derived optical front-end for low-power PSOG in smart-glasses form factors. While the present validation is limited to controlled experiments on a humanoid robot and coarse gaze-region classification, the approach represents a promising step toward always-on wearable eye tracking.

The number of devices embedding eye tracking (ET) capabilities, such as portable webcam-based consumer devices and wearable ones, such as headsets and smart eyeglasses, is rapidly increasing, making this technology truly pervasive. Despite the large number of papers and reviews discussing data quality and benchmarking of trackers, none of them is addressing the trade-off between power consumption, speed and accuracy. Power dissipation is typically dominated by signal processing to extract gaze information from sensors embedded in the glasses. This compromise is crucial for smart glasses, powered by miniature batteries, offering a typical power budget of a few tens of mW for ET. Here we propose a simple benchmarking flow for wearable trackers, focused on power consumption, as well as accuracy, precision and sampling rate, and based on three complementary test setups. We report the preliminary results of the experimental characterization of 6 commercial trackers in the first static setup and we show a comparison of their performance based on a single figure of merit.

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.