Wearable eye tracking in unconstrained settings is often compromised by slippage, yet the direct relationship between physical frame displacement and gaze error remains unexplored. This study addresses this gap combining eye tracking and motion capture to evaluate the slippage robustness of Pupil Labs Neon, Tobii Pro Glasses 3 and ViewPointSystems Lite eye trackers. Data were collected as twelve participants performed tasks involving facial expressions, induced glasses motion and locomotion. Results indicate that the Tobii Pro Glasses 3 are the most slippage-robust, maintaining an average gaze error below 2.5° regardless of movement. Conversely, the ViewPointSystems Lite exhibited large errors scaling with displacement (up to 29.3° on average) during induced motion tasks. The Pupil Labs Neon demonstrated resilience against large errors (<3.5° on average). Our findings enable researchers to anticipate the gaze error in unconstrained environments, and push manufacturers to provide realistic specifications of slippage instead of claims of “slippage-robust“ eye tracking.

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.

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.