Infrared-based eye tracking technologies suffer when strong interfering illumination is present, in particular due to sunlight when operated outdoor. Here, an alternative technology based on contactless, high-sensitivity capacitive sensing is presented, showing the design and preliminary characterization of a novel wearable eye tracking device. It is based on transparent electrodes, deposited on standard lenses, and miniaturized electronics, fitting inside the slim form factor of classic glasses. The optimization started with numerical simulations and iterative tests on prototypes of increasing integration with lenses and frames. The preliminary, though promising, results achieved with the final system show the identification of 5 regions in the visual field at a rate of 100Hz and with a power consumption of 5mW of overhead due to the capacitive sensor.

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.

Smart eyewear is emerging as an always-on platform capable of perceiving the environment and inferring intent through eye movements, making pupil tracking essential for personalized interaction. However, reliable tracking on wearable hardware remains challenging due to strict power limits and scarce annotated data. Event-based cameras offer a low-power, microsecond-latency solution, but labeled recordings still remain limited. We address this issue with a training framework that combines limited annotated real data with synthetic events and unlabeled real recordings, learning event-based pupil trackers with strong real-world generalization. We pair the U2Eyes tool with the v2e event camera simulator to generate realistic event streams, showing that networks trained on these events exhibit smaller sim-to-real gaps than networks trained on synthetic images. Moreover, our training procedure further bridges this gap, enabling our models to outperform networks trained exclusively on real data across all benchmarks, advancing toward more robust event-based eye tracking on wearable platforms.

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.

We propose a camera-free eye tracking solution for smart eyewear, leveraging a constellation of infrared photodetectors (PDs) discreetly integrated along lens edges to maintain aesthetics and increase robustness. This mechanically miniaturized design offers 16 signals per lens and significantly reduces power consumption, while retaining adequate resolution for extended reality scenarios. In fact, each lens is coupled with four PDs excited by four LEDs managed by a miniaturized and wireless processing board hosting a low-power microcontroller. A compact neural network, running in real time and trained on artificial eyes mounted on a motorized two-axis gimbal, processes 20 differential signals and achieves ∼4° gaze accuracy at 47 mW consumption and 70 Hz sampling rate. Preliminary human-eye tests confirm reliable blink detection and potential for classification of gaze direction in 5 quadrants in the visual field.

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.

Event-based cameras are becoming a popular solution for efficient, low-power eye tracking. Due to the sparse and asynchronous nature of event data, they require less processing power and offer latencies in the microsecond range. However, many existing solutions are limited to validation on powerful GPUs, with no deployment on real embedded devices. In this paper, we present EETnet, a convolutional neural network designed for eye tracking using purely event-based data, capable of running on microcontrollers with limited resources. Additionally, we outline a methodology to train, evaluate, and quantize the network using a public dataset. Finally, we propose two versions of the architecture: a classification model that detects the pupil on a grid superimposed on the original image, and a regression model that operates at the pixel level.

Event-based eye tracking represents an innovative approach to analyzing eye movements, leveraging event cameras’ high temporal resolution and asynchronous nature. This paper provides a comprehensive field review, focusing on datasets, algorithms, and challenges. It examines key datasets, highlighting their diversity and limitations, and categorizes algorithms into two core approaches: frame-based and spiking neural network (SNN)-based approaches. Frame-based methods leverage traditional techniques and deep learning, while SNNs represent an emerging field offering biologically inspired, energy-efficient solutions. However, the computational demands of emerging deep-learning methods raise questions about their feasibility for deployment on consumer-grade devices, particularly embedded systems like smart eyewear. This review provides a structured analysis of current advancements, datasets, and challenges, offering insights to guide the development of efficient and deployable event-based eye-tracking systems.

In this paper we analyze the tuning of a PIDPlus event-based control scheme with a noise-filtering event generator. In particular, different well-known PID tuning rules have been considered with different processes. A comparison with a standard PID control scheme has been also considered to evaluate the event generator effectiveness. It is shown that, independently from the employed tuning rule, the application of the devised event generator provides the advantage of reducing the variability of the manipulated variable in the presence of high-frequency measurement noise.