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.

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.

Semantic eye segmentation has proven useful in different fields, including biometrics, eye-tracking, and physiological signal extraction. For this reason, identifying the areas of an image regarding the three main parts of the eye surface (sclera, iris, pupil) is of utmost importance, as it allows one to extract various meaningful information. Most studies found in the literature focus on the development of custom Deep Learning models, with an architecture tailored specifically for eye segmentation. Moreover, for tasks such as eye-tracking, the iris and the pupil are often modeled as ellipses, thus including sections of the skin in the segmented area. In this paper, we propose a method for eye segmentation based on You Only Look Once (YOLO) models, generally employed for object detection, solving the previously described issues. First, a large version of YOLOv11 (YOLOv11L-seg) was used to perform a semiautomatic labeling of the dataset, starting from a reduced number of manually generated masks. The so-obtained dataset was then used to train two different versions of YOLO (YOLOv8 nano and YOLOv11 nano), much more compact than the model used for semi-automated labeling. The two models achieved a mean Intersection Over Union (mIOU) over the three classes of 93% and 92%, respectively, while also presenting high generalization capabilities over data acquired with different hardware compared to the current state-of-the-art models.

In an era of complex networked heterogeneous systems, simulating independently only parts, components or attributes of a system under design is not a viable, accurate or efficient option. The interactions are too many and too complicated to produce meaningful results and the optimization opportunities are severely limited when considering each part of a system in an isolated manner. The presented COSSIM simulation framework is the first known open-source, high-performance simulator that can handle holistically system-of-systems including processors, peripherals and networks; such an approach is very appealing to both CPS/IoT and Highly Parallel Heterogeneous Systems designers and application developers. Our highly integrated approach is further augmented with accurate power estimation and security sub-tools that can tap on all system components and perform security and robustness analysis of the overall networked system. Additionally, a GUI has been developed to provide easy simulation set-up, execution and visualization of results. COSSIM has been evaluated using real-world applications representing cloud (mobile visual search) and CPS systems (building management) demonstrating high accuracy and performance that scales almost linearly with the number of CPUs dedicated to the simulator.