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Contactless Dielectric Characterization of Liquids Using a Dual-Mode Microwave…

Contactless Dielectric Characterization of Liquids Using a Dual-Mode Microwave Resonant Sensor

Researchers developed a microwave resonant sensor for contactless liquid characterization in industrial, biomedical, and microfluidic applications

Dielectric characterization of liquids plays an important role in many fields. In food processing, it helps detect composition changes and contamination. In biomedical applications, it supports the analysis of fluids whose electrical properties vary with concentration. However, many established techniques still rely on bulky instruments. They also require direct contact with the sample or large liquid volumes. These limitations make them less suitable for compact, reusable, and inline measurement systems.

A Microwave Resonant Sensor for Contactless Liquid Characterization

Researchers from the University of Messina and CNR-IPCF addressed this challenge with a microwave resonant sensor for contactless liquid characterization. The device uses a planar microstrip resonator with two coupled spiral structures and a central through-hole.

Users can insert a small vial or tube into the opening. The liquid then perturbs the electromagnetic field without touching the sensing metallization. This configuration offers several advantages. It simplifies sensor reuse and reduces contamination risks. It also allows integration into pipelines, microfluidic systems, and bioanalytical platforms.

The team optimized the resonator geometry to generate two closely spaced resonant modes between 3.5 and 4.0 GHz. This dual-mode operation improves sensing performance. It could also support future self-calibrating measurement strategies.

Inkjet-Printed Prototype and Experimental Validation

The researchers fabricated the prototype through inkjet printing technology. They used a Rogers RO4003C microwave substrate and conductive silver nanoparticle ink.

To validate the sensor, the team prepared water–ethanol mixtures. Researchers often use these mixtures because water and ethanol show significantly different permittivity values. The team placed the samples inside a 6 mL vial and positioned it in the resonator hole. They then measured the sensor response through the S21 transmission coefficient.

The two resonances partially overlapped. To overcome this issue, the researchers implemented a complex-domain fitting procedure. The method combines two Lorentzian functions with a background term. This approach enabled a more reliable extraction of resonance frequency, amplitude, and quality factor.

Sensor Performance and Sensitivity

The experimental results confirmed the effectiveness of the proposed microwave sensing platform.

As ethanol concentration increased, both resonant modes shifted toward higher frequencies. This trend reflects the reduction in the effective permittivity of the mixture. The sensor achieved sensitivities of approximately 20.1 kHz/% and 20.5 kHz/% ethanol for the two resonant modes.

The second resonance showed particularly strong linearity, with an R² value of 0.99. The amplitude variation of one resonance also provided additional information about the liquid composition. Furthermore, repeatability and reproducibility tests revealed only limited parameter dispersion.

Future Applications of Microwave Liquid Sensing

The proposed approach could support several future developments. Researchers could adapt the platform to flowing liquids and implement it on flexible substrates. They could also extend the method to retrieve complex permittivity parameters more completely.

These advances could enable new applications in industrial process monitoring, microfluidics, and non-contact analysis of biological samples.

The research appeared in the MDPI Sensors journal. Readers can find additional details at https://www.mdpi.com/1424-8220/26/5/1544.

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A Self-Calibrated Resonant Sensor for Dielectric Characterization Based on…

A Self-Calibrated Resonant Sensor for Dielectric Characterization Based on Mode Splitting

A novel differential approach developed at the University of Messina reduces environmental effects in dielectric measurements

Dielectric characterization of materials plays a crucial role in several application domains, ranging from biomedical devices to industrial monitoring systems. However, conventional dielectric characterization techniques are often bulky, expensive, and require complex instrumentation.

Planar Resonators as an Alternative Solution

Researchers have introduced planar resonators as a promising alternative to conventional dielectric characterization techniques. Engineers can fabricate these devices using standard PCB manufacturing technologies, while their inherent compatibility with wireless systems enables contactless measurements. Despite these advantages, environmental factors such as temperature fluctuations and humidity variations often affect resonant sensors, reducing measurement accuracy.

A Self-Calibrated Resonant Sensor Based on Mode Splitting

To address this challenge, a research group from the University of Messina (Italy), led by Prof. Nicola Donato, member of Res4Net, developed a novel self-calibrated resonant sensor for dielectric characterization. The proposed device exploits the mode-splitting phenomenon to perform differential measurements and automatically compensate for environmental effects.

The research team designed the sensor around a planar ring resonator operating in the sub-GHz frequency range. A geometric asymmetry generates mode splitting, producing two orthogonal resonant modes. The first resonance (f₁) responds to changes in the relative permittivity (εr) of the material under test, while the second resonance (f₂) shows minimal dependence on the sample and acts as an internal reference.

Because environmental variations influence both resonant modes in a similar way, the frequency difference between them (Δf = f₂ − f₁) effectively suppresses common-mode disturbances while preserving sensitivity to changes in relative permittivity. This differential measurement strategy allows the sensor to maintain reliable performance even under varying environmental conditions.

Prototype Fabrication and Experimental Validation

The researchers fabricated the prototype using the Voltera NOVA inkjet printing prototyping system. They validated the device inside a controlled environmental chamber over a temperature range from 21 °C to 50 °C. The results showed that both f₁ and f₂ experienced similar linear frequency shifts with temperature. In contrast, the differential parameter Δf exhibited a significantly lower temperature dependence, with a slope approximately four times smaller than those of the individual resonances.

Results and Future Perspectives

These findings demonstrate the effectiveness of the proposed self-calibrated resonant sensor and highlight the potential of the mode-splitting approach for robust dielectric characterization in real-world environments.

The authors published the research in the IEEE Transactions on Instrumentation and Measurement.

For further details, readers can access the full paper at: https://ieeexplore.ieee.org/document/11052665

News

A Journey Beyond Research: My Experience at ASIPP Through…

A Journey Beyond Research: My Experience at ASIPP Through the TRUST Project

I am Letizia, a PhD student at the University of Tuscia working in the field of nuclear fusion. In May last year, I had the opportunity to visit the Institute of Plasma Physics of the Chinese Academy of Sciences (ASIPP) in Hefei, China.

This experience was made possible through the TRUST Project, which brought our research group to ASIPP to explore ongoing activities, strengthen collaboration with Chinese colleagues, and exchange knowledge in the field of nuclear fusion research.

Visiting Major Fusion Research Facilities

During the visit, we had the chance to see some of ASIPP’s major facilities, including CRAFT, the future site of BEST, and the operating EAST tokamak.

Beyond the technical visits, we shared our own work and discussed common challenges, opportunities, and future perspectives. Experiences like this clearly demonstrate how scientific progress depends on international collaboration, knowledge exchange, and the ability to build connections across different countries and research environments.

Preparing for the Journey

What I felt most strongly was curiosity. The idea of experiencing a culture so different from my own was both fascinating and challenging.

Everything—from daily life to communication—would be new, and the language barrier seemed like the biggest obstacle. Yet, that was also what made the experience so meaningful.

Research Exchange and New Perspectives

Meeting PhD students and researchers working on topics closely related to my own research was one of the most valuable aspects of the experience.

We exchanged ideas, discussed different approaches, shared doubts, and offered suggestions. It was a powerful reminder that, even when we come from different parts of the world, we are often asking the same scientific questions and working towards the same goals.

At the same time, being immersed in such an advanced research environment was incredibly stimulating. It encouraged me to look at my work from new perspectives and provided fresh motivation for my future research activities.

Discovering a New Culture

Outside the research setting, there was also the rewarding experience of discovering a new culture.

Observing everyday habits, traditions, and ways of interacting made me realize how much there is to learn beyond the academic environment. Sharing these moments with colleagues and new friends made the experience even more vivid and memorable.

A Personal and Professional Growth Experience

Looking back, this journey was not only a scientific visit but also a personal challenge and an important opportunity for professional growth.

It pushed me beyond my comfort zone, allowed me to connect with people and ideas that once felt distant, and reinforced the value of international collaboration in advancing nuclear fusion research.

News

Machine Learning for Early Prediction of CognitiveDecline in Alzheimer’s…

Machine Learning for Early Prediction of Cognitive Decline in Alzheimer’s disease

Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by a long preclinical phase during which cognitive impairment develops gradually. Current therapeutic strategies are only able to slow disease progression, making early identification of subjects at risk a crucial challenge. Mild cognitive impairment (MCI) represents an intermediate stage between normal cognition and dementia and is widely recognized as a key target for early diagnosis and preventive intervention. However, early clinical manifestations are often subtle and insufficient for reliable diagnosis. In this context, Machine Learning (ML) techniques, especially when combined with multimodal biomarkers, offer promising tools to improve early prediction of cognitive decline.

Objectives: The main objective of this study is to develop an interpretable Machine Learning model capable of predicting the transition from cognitively normal (CN) status to mild cognitive impairment (MCI) using baseline multimodal biomarkers. By leveraging data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the study aims to:

identify the most informative biomarkers associated with early cognitive decline;
assess the predictive performance of a Random Forest classifier;
support early risk stratification in individuals without overt clinical symptoms.

Methods: The study is based on data from the ADNI Merge dataset, which includes longitudinal observations from more than 2,400 subjects characterized by demographic variables, cognitive assessments, cerebrospinal fluid biomarkers, PET imaging, and radiomic features. Only subjects CN at baseline with at least one follow-up visit were considered, focusing on stability versus progression to MCI. A structured preprocessing pipeline was implemented. Age at each visit was reconstructed, categorical variables were encoded, and diagnostic labels were harmonized. Missing values were systematically analyzed to assess data quality, as can be seen in Figure 1.

Features with more than 33% missing values were removed, while remaining missing data were imputed using a k-Nearest Neighbors approach. Exploratory analysis was performed through correlation matrices to investigate relationships between biomarkers.

Statistical analysis based on ANOVA was applied to identify variables showing significant differences between CN and MCI groups. An example is shown in Figure 3.

Finally, an interpretable Random Forest classifier was then trained using baseline features, with hyperparameters optimized to balance sensitivity and specificity.

Results: The proposed Random Forest model achieved an overall classification accuracy of 76%, with a sensitivity of 64% and a specificity of 84% in predicting progression from CN to MCI. Exploratory and statistical analyses highlighted that cognitive test scores (e.g., RAVLT, ADAS, mPACC) provided the strongest discriminative power, supported by neurostructural radiomic features such as ventricular and hippocampal volumes.

Conclusions: This study demonstrates the effectiveness of an interpretable Machine Learning approach for the early prediction of cognitive decline in Alzheimer’s disease. By integrating multimodal baseline biomarkers, the proposed Random Forest model provides reliable performance in identifying individuals at risk of progression to MCI. The results support the use of data-driven methods to enhance early diagnosis and risk stratification, contributing to the development of personalized and preventive strategies in the management of neurodegenerative diseases.

References:

[1] L. De Palma, A. Di Nisio, A. M. Lucia Lanzolla, P. Matarrese, E. M. Pich and F. Attivissimo, “Machine Learning for Early Prediction of Cognitive Decline in Alzheimer’s Disease,” 2025 IEEE Medical Measurements & Applications (MeMeA), Chania, Greece, 2025, pp. 1-6, doi: 10.1109/MeMeA65319.2025.11068006.

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Enhancing ABP Estimation through Comprehensive PPG Signal Analysis and…

Enhancing ABP Estimation through Comprehensive PPG Signal Analysis and Advanced Loss Function Optimization

Background: Blood pressure (BP) is a fundamental physiological parameter for the diagnosis and management of cardiovascular diseases, particularly hypertension. Conventional cuff-based measurement techniques provide only intermittent values and are not suitable for continuous monitoring. Photoplethysmography (PPG) has emerged as a promising non-invasive technique for continuous BP monitoring, especially in wearable devices and telemedicine applications. However, accurate arterial blood pressure (ABP) estimation from PPG signals remains challenging due to noise, motion artifacts, and strong inter-subject variability. Deep Learning (DL) models offer powerful tools to capture the complex nonlinear relationship between PPG and ABP signals, but their performance is strongly influenced by the choice of the loss function.

Objectives: The main objective of this study is to enhance the accuracy and clinical relevance of ABP estimation from PPG signals by introducing a dedicated loss function that incorporates physiological knowledge. Specifically, the study aims to:

develop DL-based regression models for ABP estimation using raw PPG signals;
design a novel loss function that emphasizes systolic and diastolic peaks of the ABP waveform;
evaluate the proposed approach in an inter-subject framework to assess its robustness and generalization capability.

Methods: The proposed methodology is based on data extracted from the MIMIC-III waveform database, which contains synchronized PPG and ABP signals acquired from intensive care unit patients. After subject selection, signals were preprocessed through temporal alignment, noise reduction, normalization, and quality assessment. The overall preprocessing and dataset construction workflow is summarized in Figure 1.

Several DL architectures were implemented, including Residual U-Net and Long Short-Term Memory (LSTM) networks adapted for one-dimensional signal regression. Models were trained using the Adam optimizer and evaluated using mean absolute error and root mean squared error metrics. To improve prediction accuracy at clinically relevant points, the proposed Peak Enhancing Loss Function (PELF) was introduced. The loss function assigns higher weights to systolic and diastolic points of the ABP waveform, as illustrated by the weighting profile in Fig. 2, thus guiding the model toward more accurate peak reconstruction.

Results: The performance of the proposed Peak Enhancing Loss Function (PELF) was quantitatively evaluated and compared with the conventional Mean Squared Error (MSE) loss using a Residual U-Net architecture. The comparison was carried out on the test set considering two different signal durations, 8.192 s (Dataset A3) and 30 s (Dataset A4), using PPG signals and their derivatives.

The results reported in Table 1 show that the adoption of PELF leads to an overall improvement in BP estimation accuracy with respect to MSE. In particular, for the 30 s signal configuration (Dataset A4), PELF achieves the best performance, with a reduction of both RMSE and MAE for systolic and diastolic pressure estimation. Specifically, the RMSE for systolic pressure decreases from 17.75 mmHg (MSE) to 17.38 mmHg (PELF), while the MAE for systolic pressure is reduced from 14.13 mmHg to 13.67 mmHg. A similar improvement is observed for diastolic pressure, where the MAE decreases from 6.01 mmHg to 5.72 mmHg. For the 8.192 s configuration (Dataset A3), PELF provides slightly improved systolic error metrics and marginal variations in diastolic RMSE compared to the use of MSE. These results indicate that the proposed loss function is particularly effective when longer signal segments are available, allowing the model to better exploit temporal information.

Conclusions: This study highlights the importance of loss function optimization in DL–based non-invasive BP estimation. By integrating physiological knowledge into the training process, the proposed approach improves the accuracy and reliability of ABP predictions from PPG signals, supporting the development of cuffless BP monitoring systems suitable for telemedicine and continuous health monitoring applications.

References:

[1] Luisa De Palma, Gregorio Andria, Filippo Attivissimo, Anna Maria Lucia Lanzolla, Attilio Di Nisio, Enhancing ABP estimation through comprehensive PPG signal analysis and advanced loss function optimization, Measurement, Volume 256, Part B, 2025, 118210, ISSN 0263-2241, https://doi.org/10.1016/j.measurement.2025.118210.

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Preliminary study on alternative magnetic layout (AML) for tokamaks…

A new approach to tokamaks: the study on the Alternative Magnetic Layout (AML) and the TRUST project

Research on nuclear fusion aims to make reactors more compact, efficient, and easier to build. In this context, a group of researchers from the University of Tuscia has published a preliminary study in [1] introducing a new magnetic confinement scheme, called Alternative Magnetic Layout (AML).
The idea is innovative: moving the central solenoid (CS) around the central column of the toroidal field coils (TF). This configuration reduces the reactor’s radial size, making it more compact. To compensate for the increased magnetic field required, high-temperature superconductors (HTS) are envisioned for the coils, capable of carrying higher currents and sustaining stronger fields compared to traditional superconductors.
The study shows that this solution is technically feasible, although engineering challenges remain in terms of assembly, electromagnetic force management, and integration of components. Some of the advantages include:

better efficiency in the use of internal space;
the possibility of placing poloidal field coils closer to the plasma, improving control and stability;
potential cost reduction and easier maintenance.
This concept will be tested in the Tuscia Research University Small Tokamak (TRUST), a new university tokamak under construction in Viterbo. TRUST will be a flexible, low-cost experimental platform designed to train future fusion engineers and test innovative technologies and materials.
In conclusion, AML represents a promising proposal for the future of fusion reactors: a more compact design, aimed at both academic research and technology transfer to industry.

References:

[1] S. Carusotti, et al. , Preliminary study on alternative magnetic layout (AML) for tokamaks reactors in the TRUST project framework, Fusion Engineering and Design,.

News

Which Are the Needs of People with Learning Disorders…

A new scientific paper [1] is published in collaboration between the University of Tuscia and the Blue Cinema TV company concerning the use of new immersive technologies with the aim of make learning more inclusive.
The paper firstly describe the life-size, interactive virtual human beings OLOS® character that serve as storytellers and guides in museums, providing immersive experiences without the need for sensory filters like VR headsets. OLOS® integrates audiovisual interfaces, natural language processing, and IoT capabilities, allowing users to interact via voice, touchscreen or tangible interfaces linked to real objects. The patented system delivers high-definition, life-size visualizations using an optical apparatus that creates a holographic illusion. OLOS® supports multilingual functionality and accessibility for users with disabilities, making it a scalable and sustainable solution for cultural institutions. Its event manager processes interactions using speech recognition, Q&A engines, and video responses, enhancing engagement with museum visitors. The system is already widely applied in cultural heritage and is now being expanded to support individuals with specific learning disorders.

Second, an analysis is made on two separate questionnaires, the first [2] was developed during the European project “VRAILexia” while the second created ad-hoc for this project. The VRAILexia self-analysis questionnaire was constructed as a result of informal interviews conducted on a group of dyslexic volunteers. This interview asked what were the major difficulties they encountered during their schooling, what tools they found most useful and what strategies they found most effective in dealing with them, with the aim of developing “BESPECIAL,” an artificial intelligence-based platform to recommend tools and strategies to facilitate the study of students with SLDs. The questionnaire was then submitted to more than 800 students with SLD certification. The second questionnaire, on the other hand, was used to investigate some of the youngsters preferences regarding their experiences in museums and immersive realities. The questions regarding museums ranged between preferences and difficulties, thus going into how attractive these places could be to the younger generation and whether they found it difficult to find the right information. Finally, the section dealing to immersive realities served to explore the knowledge of the youngsters regarding these new technologies and their opinion of their use in learning contexts.

Thanks to this analysis, new tools were introduced within the OLOS® system that can totally customize the experience according to their needs. Among these tools, we find some available on one’s own device or on the GUI related to the installation, such as: concept maps, to graphically display the narrators’ explanations; an illustrated dictionary, built using animations combined with simplified language; videos, to delve deeper into the topics covered; and keywords, to help fix concepts. Finally, it will be possible to activate an enriched fruition of images and videos that will appear within the holographic system, thus helping users in memorizing concepts. All these tools will be able to be recommended directly by the system having provided for the implementation of the BESPECIAL platform within OLOS®.

References
[1] Materazzini, Michele, et al. “Which Are the Needs of People with Learning Disorders for Inclusive Museums? Design of OLOS®—An Innovative Audio-Visual Technology.” Applied Sciences 14.9 (2024): 3711.
[2] Zingoni, Andrea, et al. “Investigating issues and needs of dyslexic students at university: Proof of concept of an artificial intelligence and virtual reality-based supporting platform and preliminary results.” Applied Sciences 11.10 (2021): 4624.

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Anthropology of the Algorithm: how stereotypes, biases, and cultural…

The world of artificial intelligence (AI) is rapidly evolving, but it is not without its complex challenges. The new video article titled Anthropology of the Algorithm: How Stereotypes, Biases, and Cultural Belonging Influence AI, presented by Professor Alessandra Castellani, sheds light on a crucial and often overlooked aspect of AI development: the influence of cultural biases.

In her work, Professor Castellani explores how AI is not a neutral entity but rather the product of the choices, experiences, and beliefs of its creators. Through an anthropological lens, she examines how algorithms can reflect and amplify stereotypes and biases already present in human societies.

For example, many facial recognition systems have shown lower accuracy rates when applied to individuals with darker skin tones, a problem attributed to unbalanced datasets and a lack of attention to cultural diversity during their design.

Professor Castellani places particular emphasis on the ethical responsibility of developers and tech companies. “We cannot treat AI as a tool isolated from human reality,” Castellani highlights in the video. “Every algorithm is born within a cultural context that influences its design, implementation, and even its use.”

The video article is rich with practical examples, including case studies on how cultural belonging and social dynamics influence key decisions in algorithm design. Castellani invites reflection on the importance of building inclusive AI systems that respect diversity.

The central message of the video is clear: the tech community must recognize and address the role of stereotypes and cultural biases in AI. Only through a conscious and multidisciplinary approach can technologies avoid perpetuating social inequalities or injustices.

Professor Castellani’s contribution is a call to action, not only for experts in the field but also for the entire academic and industrial community.

The video article Anthropology of the Algorithm is available on the official Res4Net website and on major academic community channels. We encourage everyone interested to watch it and join the discussion on how to make AI a truly fair and inclusive tool.

Author of the Video Article: Prof. Alessandra Castellani.

Published on: Res4Net Official Channel

Duration: 19 minutes

Language: Italian (subtitles available in English)

Don’t miss this opportunity to discover a new perspective on one of the most influential technologies of our time.

News

University of Messina in the SAMOTHRACE Project

SAMOTHRACE (Sicilian Micro and Nano Technology Research and Innovation Center) is a project funded by the Italian National Recovery and Resilience Plan (PNRR) with approximately 120 million euros. It involves 28 partners, including the Sicilian universities, research institutes and industry leaders. The aim is to create a strong collaboration between experts in microelectronics, microsystems, materials and microtechnologies, with a focus on Sicily but with an eye on the global market.

The SAMOTHRACE project addresses the European Commission’s “Digital, Industry & Space” challenge, while also focusing on other key areas such as health, energy, mobility, agriculture and the environment. It supports several “Global Sustainable Development Goals”, such as promoting sustainable agriculture, improving health, achieving gender equality, ensuring access to clean energy, and promoting sustainable industrial growth.

The project started on October 2022 and will last for three years, ending on September 2025.

The University of Messina (UniME) plays a key role in the project, leading Spoke 2, which focuses on advanced systems and sensor technologies.

During the first two years of the project, UniME contributed to building knowledge and skills in micro- and nanotechnologies. This included hiring new researchers and offering PhD scholarships to train experts and develop human resources. Unime also launched funding programs to support companies and organizations engaged in industrial R&D in areas such as energy, environment, health, agriculture and smart mobility.

On November 20-22 2024, the second year review meeting of the SAMOTHRACE project was held in Palermo. Participants, including UniME, presented their research and the results achieved during the year. A video summarizing these activities is available on YouTube.

Another important meeting will take place in Palermo in March 2025. It will focus on the activities carried out and the innovative prototypes developed in the project. Detailed information about this meeting will be made available shortly on the official SAMOTHRACE website: www.samothrace.eu.

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Non-contact Measurement of Intraocular Pressure (IOP) Via Corneal Deformation…

The Electrical and Electronic Measurement Research Group of Politecnico di Bari, in collaboration with the Optics BioTech Lab of the University of Maryland, has proposed a novel approach for assessing the performance of eye blink dynamics related to intraocular pressure (IOP). This joint research project aims to develop a non-contact method for measuring IOP, thus improving current techniques that need direct contact with the eye and limiting the clinical requirements for IOP assessment.

Reference: Non-contact Measurement of Intraocular Pressure (IOP) Via Corneal Deformation Induced by Natural Blinking (optica.org)

The study of eye blink dynamics is critical for understanding various ocular conditions, especially in the context of intraocular pressure (IOP). Traditional methods of assessing IOP are invasive and require direct contact with the eye. This research explores a non-contact method for evaluating eye blink dynamics to infer IOP, based on the force exerted by the eyelid during blinking.

The imaging system used in this study consists of an ophthalmology slit lamp equipped with an RGB camera, capable of capturing images at 130 frames per second (FPS). The camera is positioned orthogonally to the participant’s line of sight to capture lateral eye images. The images are acquired with a field of view of 510×638 pixels.

We sought a natural method to increase IOP in a healthy participant to investigate the difference in eye-blink dynamic between normal and elevated IOP. The Valsalva maneuver was identified as an appropriate solution, as it naturally elevates IOP. This maneuver involves forceful expiration against a closed glottis. During the experiment, participants were instructed to blow for 15 seconds while maintaining a pressure of 40 mmHg, measured with an analog manometer. The increase in IOP due to the Valsalva maneuver was verified using the iCare IC200 portable tonometer. Typically, IOP increases from 18 to 25 mmHg in a healthy participant. The experiment included two eye-blinking sessions with the same participant. In the first session, 17 normal blinks and 13 Valsalva-induced blinks were recorded, while the second session included 10 normal blinks and 10 Valsalva-induced blinks.

We hypothesize that the eye behaves like a spring, where the displacement is directly proportional to the force applied. Specifically, when intraocular pressure (IOP) is elevated, the eye is subjected to greater force, resulting in faster movement than normal IOP levels. To analyze this, we fit the corneal displacement using a first-order system response. Thus, the metrics used to see if there is a statistically significant difference between normal and Valsalva blinks are widely employed for first-order systems:

Time constant : indicates how quickly the eye opens.
Rise time: measures the time required for the eye to transit from a partially open state (10 %) to a nearly fully open state (90%).
Bilinear approximation: we approximate the first order system response using two lines and , where and are the slopes and the intersection point of these two lines has coordinates

To determine if there is a statistically significant difference between normal and Valsalva blinks, we performed a two-sample t-test with unequal variance and a significance level of . As anticipated, the dynamics of normal blinks are slower than those of Valsalva blinks. Specifically, the mean values of the time constant, rise time, and the abscissa of the intersection point for normal blinks are higher than those for Valsalva blinks, while the opposite is true for the slope of the first line of the bilinear approximation, . Additionally, the mean values of these metrics for the first session are close to those of the second session, strongly indicating the repeatability of this process.