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.

CUPIDO (Circuit for Unobtrusive Palpebral Interpretation and Detection Optimization) is an ultra-low-power electrostatic sensor able to convert eye blinks into digital events with a detection sensitivity up to 90.5 %. It can be easily integrated into the rims of smart glasses allowing for contactless interaction without compromising comfort and privacy (since no camera is used). Thanks to its extremely low power consumption (385.1 μW at peak during the blink), CUPIDO can extend battery life in smart glasses, allowing for continuous and real-time (detection latency of approximately 1 ms) monitoring applications like hands-free glasses control, assistive technologies, augmented reality, and drowsiness monitoring while driving.

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.

In this tutorial we briefly review the fundamentals of analog computing. Starting from the historical use of operational amplifiers to solve differential equations, we show how today the analog approach, in combination with novel devices and paradigms such as in-memory computing, can address the energy efficiency challenges of operations such as matrix-vector multiplication, matrix inversion, and the forward pass of a decision tree or attention block. These operations are crucial for machine learning applications, especially in energy-constrained contexts.

The use of the Atomic Force Microscope (AFM) as a teaching tool in hands-on laboratory activities for electronics engineering curricula is proposed. In addition to providing nanometric resolution in topographic imaging, profilometry and metrology of micro-and nano-fabricated devices, pivotal in modern electronics, the AFM represents an excellent instrument to: (i) experiment with the negative feedback control of a sophisticated opto-electro-mechanical system, (ii) perform nanoscale current (DC) and impedance (AC) measurements and maps by means of conductive tips. Examples and results with both discrete and integrated low-noise current amplifiers are here reported.

We present a portable electronic sensor system for real-time monitoring of dust deposition in avionics and space applications, where particle contamination can alter the operation of instruments and scientific payloads. To measure and verify the deposition events on space hardware during fabrication, assembly and in the launch phase, a compact system based on an integrated electronic CMOS chip has been designed. The chip tracks the capacitance variation between two interdigitated electrodes, affected by the arrival of dust, ensuring single event monitoring and detection of particles with a diameter down to 1 µm. The system is completed by a low-power microcontroller for setting the chip operation, an analog-to-digital converter for the acquisition of the particle deposition events, a Bluetooth transmitter for data read-out and all the other hardware and software functionalities that ensure completely autonomous operations.

Parallel computing is the key to accelerate artificial neural networks, both in digital and analog implementations. Our research focuses on analog artificial neural networks (NN), where parallel computations are executed with voltages, charges and currents, using as the computing elements the same devices that act as memories for the raw processed data. These analog in-memory computing structures can be exploited for edge computing applications, thanks to their ability to directly interface with analog signals with low latency, reducing data throughput and front-end complexity. This work presents the specific implementation of a single neuron used in a larger feedforward, fully connected analog neural network ASIC (ANNA), showing its performance and criticalities. The ASIC is designed as a re-programmable analog accelerator for the reconstruction of the position of interaction of gamma rays in Anger cameras, for medical imaging applications as PET and SPECT. This first prototype has been fabricated on a 0.35 um CMOS process with an area of 24 mm2, and it is able to process 200,000 events per second, with an experimentally measured energy efficiency of 50 GOPS/W. The network has been trained on a Matlab model, that was adjusted to embed many nonidealities to match the physical chip, as demonstrated in this work.

A novel low-power and easy to integrate sensing configuration for wearable eye tracking is presented. Within the context of infrared oculography based on individual photosensors, we proposed to couple a set of photodetectors to the lateral edges of a standard lens, acting as waveguide for the IR light, instead of directing them towards the eyeball. This allows to embed the detectors in the rim, thus being fully hidden in the eyewear, invisible to the user and robustly integrated with the glasses. A preliminary setup with four photodiodes whose signals are processed by an agile two-layer neural network was realized and characterized. Here we demonstrate both experimentally and by means of simulations the feasibility of this patent-pending approach. Detected maps of light patterns respond to different impinging light orientations. An angular resolution of about 5° is achieved with only 4 individual photodetectors coupled to a thick rectangular glass lens. A larger number of detectors would provide better resolutions. The parameters of ray-tracing simulations were first adjusted to match the experimental data from a simplified geometry. Then, simulations were used to estimate the expected signals with an eye model, paving the way to a promising outlook. The combination of hardware and software solutions here presented aims at addressing the trade-off between power consumption and angular resolution in the estimation of the direction of gaze which is crucial for pervasive eye tracking.

A newly introduced laboratory experience addressing the practical understanding of the impedance of the non-Faradaic electrochemical interface, within a bioelectronics class, is presented. The project is carried out by teams of M.Sc. students (3 to 5 persons) of mixed backgrounds (electronics and biomedical engineering). It comprises two phases. First, a design phase, in which a PCB hosting planar gold coated electrodes, to be sized in order to properly measure the ionic resistance of the solution (below 10 MHz), and a low-noise transimpedance stage to interface to a USB network analyzer are designed. Then, during the experimental activity impedance spectroscopy is performed with the electrodes in saline solution, coupled to microfluidics, and data are analyzed to estimate the solution conductivity and compared with simulations.

In this paper, the authors aim to design a decision support system (DSS) based on machine learning (ML) to assist institutions in implementing targeted countermeasures to combat and prevent emergencies such as the COVID -19 pandemic. The DSS relies on an ensemble of several ML models that combine heterogeneous data to predict risk levels at the micro and macro levels. Some preliminary analyses have already been conducted showing the corre-lation between nitrogen dioxide (N0O), mobility-related parameters, and COVID -19 data. However, given the complexity of the virus spread mechanism, which is re-lated to many different factors, these preliminary stud-ies confirmed the need to perform more in-depth analyses on the one hand and to use ML algorithms on the other hand to capture the hidden relationships between the huge amounts of data that need to be processed.

A pervasive diffusion of wireless sensors distributed in the environment can be sustained by a significant reduction of the cost of the transducers. Water represents a fundamental resource whose management can be optimized by real-time monitoring, especially in agriculture (irrigation). To this aim we present a low-cost flexible pipe sheath to detect water leakage, as well as conductivity and temperature, by means of contactless impedance measurements. Finite-element simulations show the potential to detect water leaks as small as 1 liter across a pipe length of 2 m, corresponding to a 1 % change of a 5 kΩ impedance at 1 kHz, easily detectable by off-the-shelf impedance chips.

A safety impedance micro-sensor based on flexible electrodes for continuous monitoring of the surface cleanness of pipes of small diameter (down to few mm) handling drinkable fluids is presented. It allows discrimination between biofilm and limescaling, grants high resolution (few μm in thickness) and enables early warning and predictive (closed-loop) maintenance. It is coupled with a temperature and fluid conductivity probes in order to calibrate for their fluctuations. The current prototypal electronics, measuring impedance in the MHz range, can be scaled towards low-cost and low-power pervasive implementations. Preliminary experimental results showing the consistence with non-curved geometries are presented.

A miniaturized 16-channel readout system for portable γ-ray spectroscopy and imaging is presented. It features the combination of a microcontroller with a novel CMOS ASIC for analog processing of the currents of planar SiPM solid-state photodetectors coupled to large scintillators. The adoption, in the gated-integrator shaper, of a doublewindow time-based automatic gain control offers an 84 dB dynamic range (3 μΑ to 30 mA) combining single-photon sensitivity with an extended energy range (50 keV – 10 MeV). Sensitivity (SNR ∼7dB for 1 photoelectron), linearity of 4%, gain switching among 3 integrator capacitors, 3.8% energy resolution (at 662 keV) and imaging capability with an array of 144 selectively-merged pixels are experimentally demonstrated.

A novel impedance micro-sensor measuring with 0.5 μm resolution the thickness of undesired chemical or bacterial films fouling the surfaces of in-line probes, monitoring water ducts, is presented. The circuit for lock-in impedance detection (operating at 5 MHz, 10-bit resolution) is illustrated. It is embedded in a multi-sensor IoT water quality monitoring node, enabling self-diagnostics and on-demand maintenance. The credit-card-sized board features pH, temperature, 4 conductivity probes and the film monitor, along with anti-bubble actuation and wireless GSM transmission. The additional power consumption added by the sludge sensor is only 5% of the total.

A mixed-signal electronic system allowing closed-loop control of 16 independent integrated photonic devices equipped with CLIPP transparent optical probes (-35 dBm sensitivity, 50 kHz speed) is presented. It features a 32-channel CMOS lock-in front-end (203 nV /√Hz noise, 100 dB dynamic range), interfaced via conditioning chains to multiple ADCs and DACs driven by a Xilinx Spartan-6 FPGA for real-time processing, including the generation and demodulation of multiple pilot tones for channel labeling and dithering-based feedback. The results of the platform characterization are reported, along with the first application of automatic control applied to a novel all-optical unscrambler for mode-division multiplexing.