The growing demand for wearable and Internet of Medical Things (IoMT) devices is driving the development of compact, low-power platforms for continuous physiological monitoring. Bioimpedance analysis represents a versatile non-invasive technique for the assessment of tissue properties, body composition, and respiratory dynamics. This work presents a comprehensive experimental validation of a compact bioimpedance measurement platform based on the SENSIPLUS chip, a CMOS sensor interface integrating a frequency-programmable lock-in amplifier for Electrochemical Impedance Spectroscopy in the 10 kHz-1 MHz range. The platform was validated at three complementary levels: (i) electrical characterization on Debye tissue-equivalent circuits using a three-point bilinear calibration, with analysis of the electrode-skin contribution and repeatability assessment; (ii) in vivo multi-frequency bioimpedance spectroscopy (BIS) with Cole-Cole model fitting and hook-effect correction; and (iii) single-frequency thoracic impedance plethysmography for respiratory monitoring. Results were compared against an Agilent E4980A precision Inductance (L), Capacitance (C), and Resistance (R) meter and a calibrated spirometer. The presented device achieved a maximum resistance error below 5.7% and reactance deviation under 6 Ω across the investigated frequency range, Cole-Cole parameters consistent with reference values, and strong linear correlation (R2=0.97) between thoracic impedance variations and tidal volume, with respiratory rate estimation errors below 2% across the ten sessions, specifically 1.43% during normal breathing and 1.96% during deep breathing. These results demonstrate that the SENSIPLUS-based platform achieves metrological performance compatible with the requirements of wearable IoMT applications, here demonstrated in a single-subject proof-of-concept study, while relying for all critical analog functions on a compact (1.5×1.5) mm2 system-on-chip with low power consumption (1.5 mW).

A 24 GHz radar, useful for remote vital signs monitoring, operating in linear/circular polarization (LP/CP), was developed to analyze vital signs detection performance in the presence of environmental clutter. The radar system, equipped with horn antennas with and without suitable LP-CP field polarization converters, is designed for monitoring respiratory and cardiac activity in domestic and hospital environments. A geometric optics (GO) model is introduced to show the advantages of circularly polarized (CP) radar sensors over linearly polarized (LP) ones in discriminating vital signals in scenarios where vibrating metallic panels simulate environmental disturbance (worst case). An analytical model, numerical computations, and experimental investigations highlighted the robustness of CP radar sensors in detecting vital signs in the presence of environmental clutter.

This work presents the design and preliminary experimental validation of deformable sensors based on time-domain reflectometry (TDR) for rehabilitation-oriented interaction monitoring. Three architectures were investigated: a planar multilayer sensor and two coaxial configurations based on foam and engineered TPU–Hilbert structures. Controlled indentation tests were performed at different positions and deformation levels, extracting two TDR-derived features: the minimum reflection coefficient ρmin, related to deformation intensity, and the perturbation time tpert, related to contact localization. Preliminary calibration curves and two-dimensional maps were used to analyze the coupled dependence of the response on position and indentation depth. Application-oriented manual tests confirmed the different suitability of the three geometries for localized finger pressing, distributed two-hand grasping, and controlled single-hand squeezing. Overall, the results support TDR-based deformable sensors as low-complexity and geometry-adaptable tools for spatially resolved monitoring of motor rehabilitation interactions.

This paper addresses the importance of performing an accurate electrical characterization of dielectric materials for 3D printers before using them in the design of printed components for microwave applications. The great versatility of 3D printers has enabled their use for easy and cost-effective manufacturing in the field of electromagnetic systems, particularly in the areas of antennas (dielectric and lens antennas) and RF sensors. In these fields, for a correct design of the printed RF devices, using the nominal value of the dielectric permittivity provided by the vendor is not enough, as the electrical specifications may be affected by various factors, such as the print filling, color, print quality, etc. This study addresses this problem with reference to the design of a linear-to-circular field converter for radar systems used to detect vital signs. Since the performance of polarization converters is highly sensitive to the actual electric characteristics of the material used for their manufacturing, it is shown that only making preliminary measurements of the permittivity of the adopted dielectric allows to meet the design constraints. Finally, measurements taken on test RF converter prototypes confirm the effectiveness of the design procedure.

This study presents a wireless, non-invasive sensing system for monitoring the dielectric permittivity of materials, with a particular focus on applications in cultural heritage conservation. The system integrates a passive split-ring resonator tag, electromagnetically coupled to a compact antipodal Vivaldi antenna, operating in the reactive near-field region. Both numerical simulations and experimental measurements demonstrate that shifts in the antenna’s reflection coefficient resonance frequency correlate with variations in the dielectric permittivity of the material under test. A calibration curve was established using reference materials—including low-density polyvinylchloride, polytetrafluoroethylene, polymethyl methacrylate, and polycarbonate—and validated through precise permittivity measurements. The system was subsequently applied to wood samples (fir, poplar, beech, and oak) at different humidity levels, revealing a sigmoidal relationship between moisture content and permittivity. The behavior was also confirmed using a portable and low-cost setup, consisting of a point-like coaxial sensor that could be easily moved and positioned as needed, enabling localized measurements on specific areas of interest of the sample, together with a miniaturized Vector Network Analyzer. These results underscore the potential of this portable, contactless, and scalable sensing platform for real-world monitoring of cultural heritage materials, enabling minimally invasive assessment of their structural and historical integrity. Moreover, by enabling the estimation of moisture content through dielectric permittivity, the system provides an effective method for early detection of water-induced deterioration in wood-based heritage items. This capability is particularly valuable for preventive conservation, as excessive moisture—often indicated by permittivity values above critical thresholds—can trigger biological or structural degradation.

Continuous respiratory monitoring is essential for the early detection and management of respiratory diseases such as chronic obstructive pulmonary disease (COPD), asthma, and sleep apnea. Traditional methods, including spirometry and polysomnography, remain the gold standard but present limitations such as high costs, invasiveness, and the requirement for trained personnel. This study proposes a multi-modal respiratory monitoring system integrating bioimpedance (BIOZ) and acoustic sensing for non-invasive, continuous, and real-time respiratory assessment. The system uses the SENSIPLUS microchip (developed by Sensichips s.r.l.) for bioimpedance measurements and a MEMS (Micro Electro-Mechanical System) microphone for capturing respiration sounds. The experimental validation was conducted by comparing these methods with a spirometer as the reference standard. Results demonstrated a strong correlation between the BIOZ and the acoustic-derived signals with spirometric data. The Root Mean Square Error (RMSE) values with respect to a gold standard, for inspiration and expiration using bioimpedance were 0.174 s and 0.190 s, respectively, while the acoustic-based approach yielded RMSE values of 0.4136 s for inspiration and 0.2217 s for expiration. These findings highlight the feasibility of integrating bioimpedance and acoustic sensing for accurate and wearable respiratory monitoring, offering potential applications in telemedicine, personalized healthcare, remote patient monitoring, early disease detection, and smart healthcare solutions.

A new modulation scheme for frequency-modulated continuous-wave (FMCW) radars with millimeter-level target motion detection capability is presented. The proposed radar scheme is free from the synchronization constraint and exhibits low sensitivity to internal parasitic mutual coupling, thus significantly reducing its design complexity without worsening its performance in terms of accuracy and operating ranges. Alternatively to canonical FMCW radars, which exploit chirp signals with triangular or sawtooth-like frequency variation, a radar based on a sinusoidal frequency modulation, which does not require specific synchronization procedures to achieve accurate motion detection even at a short distance from the radar, was developed. Both numerical and experimental results, performed with a 24 GHz radar, have shown the suitability of the proposed modulation scheme for monitoring very small target movements, consistent with those typically exhibited by the human thorax during basic vital activities (heartbeat and respiration). This makes the proposed radar scheme a suitable solution for contactless heart and breath rate monitoring.

(1) Background: An optical simulator able to provide a repeatable signal with desired characteristics as an input to a photoplethysmographic (PPG) device is presented in order to compare the performance of different PPG devices and also to test the devices with PPG signals available in online databases. (2) Methods: The optical simulator consists of an electronic board containing a photodiode and LEDs at different wavelengths in order to simulate light reflected by the body; the PPG signal taken from the chosen database is reproduced by the electronic board, and the board is used to test a wearable PPG medical device in the form of earbuds. (3) Results: The PPG device response to different average and peak-to-peak signal amplitudes is shown in order to assess the device sensitivity, and the fidelity in tracking the actual heart rate is also investigated. (4) Conclusions: The developed optical simulator promises to be an affordable, flexible, and reliable solution to test PPG devices in the lab, allowing the testing of their actual performances thanks to the possibility of using PPG databases, thus gaining useful and significant information before on-the-field clinical trials.

Bioimpedance has emerged as a versatile and non-invasive diagnostic methodology for monitoring various physiological conditions of the human body. In this context, the detailed characterization of sensors dedicated to bioimpedance measurements is crucial to ensure the accuracy and reliability of the obtained results. In this paper a comprehensive study is proposed, suggesting the use of the SENSIPLUS chip, developed by Sensichips s.r.l., as a multifunctional microchip equipped with a precision LCR meter for low-noise impedance measurements up to 2.5 MHz, to measure inductance (L), capacitance (C) and resistance (R). Through comparative analysis with a professional LCR meter, measurements were performed on electrical circuits resembling the frequency behavior of human tissues. The reported results show a good overlap for both the resistance and reactance values, with errors well within tolerance limits. Moreover, an in-depth analysis of measurement repeatability highlights consistency and reproducibility, reinforcing the reliability of the chip in bioimpedance measurements. This study represents a promising step towards the integration in wearable devices of the SENSIPLUS chip as highly-accurate bioimpedance sensor for applications in the field of Internet of Medical Things (IoMT).

This article presents the metrological characterization of a four-electrode compact system able to measure the dielectric properties of biological tissues at extremely low and ultralow frequencies, where data available from the literature are very limited. The cell constant k of the system, together with its expanded uncertainty, is found measuring different saline solutions of known conductivity. Since the cell constant plays a key role in the determination of tissue dielectric properties, it has been further verified through tests on other saline solutions, containing a different type of solute, confirming the accuracy of the system. In particular, results on a saline solution with a given molar concentration of KCl and on a physiological solution (Eurospital 0.9% NaCl) show that the system maximum relative error is lower than 3.3%. Therefore, it can be concluded that the system correctly measures the conductivity in saline solutions, and the parameter k can be properly used for the measurement of dielectric properties of biological tissues. As an application example, the system is used to perform measurements on bovine liver. Liver conductivity measurements show a constant behavior as a function of frequency in the examined range. Furthermore, the comparison of our results with the few data found in the literature at low frequencies shows good agreement. These observations point out the feasibility and convenience of the proposed method for the measurement of the conductivity at very low frequency.

Recent advancements in medical technology have led to personalized and proactive healthcare. Telemedicine has revolutionized patient-physician communication, allowing for remote consultations and diagnostics. Bioimpedance-based analysis has emerged as a versatile diagnostic methodology for continuous monitoring of several physiological conditions of the human body. This paper investigates the suitability of the SENSIPLUS chip, developed by Sensichips s.r.l., for analyzing body composition. The microchip is engineered to serve as a versatile device, incorporating a high-precision LCR meter tailored for low-noise impedance assessments up to 2.5 MHz. It is utilized for measuring body impedance at frequencies spanning from 10 kHz to 1 MHz, employing adhesive electrodes arranged in a tetrapolar configuration. The presented work demonstrates the feasibility of the device for non-invasive body composition quantification in a volunteer candidate. The measurements are fitted using the Cole-Cole model, which establishes the primary parameters for estimating characteristics of the human body. Moreover, an extensive analysis of measurement repeatability illustrates consistency and reliability, thereby enhancing the credibility of the chip in bioimpedance measurements. This study is a promising advancement in integrating the SENSIPLUS chip into healthcare devices. The chip serves as a highly accurate bioimpedance sensor in the field of telemedicine and of Internet of Medical Things (IoMT) applications.

Mental disorders, encompassing conditions like depression, pose a significant global health challenge. Traditional diagnostic methods, while valuable, have inherent limitations, necessitating the exploration of innovative approaches. In this study, we delved into the analysis of grapho-phonological data as potential objective indicators of depression. Our research journey began with an analysis of an existing database, containing handwriting and drawing data from both healthy individuals and those diagnosed with depressive disorders. Leveraging this data, we developed machine learning models designed to distinguish between these two groups. Notably, we observed that models utilizing drawing-related features outperformed those relying on writing features. This section of our study highlights the potential of combining these data for enhanced depression detection. The creation of a new database is a noteworthy addition, providing grapho-phonological data (i.e., handwriting, drawing and laughter data) from healthy and depressed subjects. The analysis of these data sets reached peak performance, achieving superior accuracy values in comprehensive classification models, underscoring the potential of this multi-modal approaches. The study also presents a streamlined, non-invasive protocol that allows to efficiently gather essential grapho-phonological data, taking only approximately 7 min per participant. Moreover, our work introduces the “Voice & Drawing App”, an accessible tool enabling remote data collection for mental health assessment. This innovation aligns with telemonitoring trends and offers a user-friendly solution, whether within clinical settings or patients’ homes. An accelerometer-based inertial measurement units system (IMUs) to capture motion patterns has been introduced into the app. This choice is driven by the recognition of the valuable information encoded in psychomotor behaviour, that can serve as indicators of depression. Our study highlights promising avenues for future research, including expanding subject populations, implementing automatic laughter recognition, and incorporating video data to capture facial expressions. Wearable devices with IMUs offer exciting opportunities for comprehensive depression assessment. In summary, our research seeks to improve our understanding of depression by exploring innovative approaches that incorporate graphological signals, laughter, and IMUs. These multidimensional strategies aim to enhance the accuracy of depression detection and telemonitoring, ultimately facilitating more timely and effective interventions in mental health care.

A frequency-modulated continuous-wave radar for short-range target imaging, assembling a transceiver, a PLL, an SP4T switch, and a serial patch antenna array, was realized. A new algorithm based on a double Fourier transform (2D-FT) was developed and compared with the delay and sum (DAS) and multiple signal classification (MUSIC) algorithms proposed in the literature for target detection. The three reconstruction algorithms were applied to simulated canonical cases evidencing radar resolutions close to the theoretical ones. The proposed 2D-FT algorithm exhibits an angle of view greater than 25° and is five times faster than DAS and 20 times faster than the MUSIC one. The realized radar shows a range resolution of 55 cm and an angular resolution of 14° and is able to correctly identify the positions of single and multiple targets in realistic scenarios, with errors lower than 20 cm.

Use of electromagnetic (EM) fields in the food industry for food inspection as well as sterilization and drying is increasing. To develop such applications, the knowledge of the dielectric properties of food is fundamental. In the literature, there is scarcity of data on dielectric properties of powdered foods, with reported values that are usually at specific frequencies only. This article reports the development of an experimental framework to characterize the dielectric properties of powdered food over a wide range of frequencies. The used methodologies include the open-ended coaxial probe technique and waveguide systems: the first being intrinsically a broadband technique but strongly dependent on the type of material being tested, and the latter being a medium- to narrowband technique allowing a better control of the tested material. The proposed framework is then used to measure the dielectric properties of several types of flour between 500 MHz and 20 GHz. The measured properties are then characterized in terms of density and interpolated with a Cole–Cole dispersive model. From the measured results, the suitability of the proposed setup was demonstrated. Additionally, it was verified that the permittivity of flour does not depend on the brand and can be expressed as a function of the density of the material, following a mixture formula already proposed in the literature. Moreover, the proposed Cole–Cole fit is able to represent the frequency behavior of the real part of permittivity of flour with differences within 3% from the measured data.

An antenna operating between 300 MHz and 700 MHz, designed to be used on a ground penetrating radar installed on an Unmanned Aerial Vehicle (UAV) for the exploration and characterization of the buried ice deposits on Mars, is presented.To this end, a lightweight, high-gain Vivaldi antenna having compact dimensions and high operating bandwidth has been taken into consideration.This antenna, equipped with circular-loaded rectangular slots etched on its radiating arms, exhibits improved performance in terms of size, return loss, gain, and fidelity factor with respect to a conventional antipodal Vivaldi antenna.Experimental measurements performed on a prototype of the Vivaldi antenna with slots showed a return loss lower than -12 dB with realized gains between 4 dBi and 6.5 dBi in the 300-700 MHz frequency band.

A new configuration of dielectric-loaded resonator (DR), particularly versatile for the complex permittivity measurement of substrates for microwave circuits, even in the presence of back metal plates, is shown here. To test this technique in a wide interval of the values of the complex permittivity, the versatility of 3-D printing is exploited to print samples with different densities, thus artificially changing the effective permittivity in the interval (1.7–3.1) for the real part and (0.02–0.06) for the imaginary part. The designed resonator, tuned at ~12 GHz, is experimentally validated by the comparison of measurements obtained on these samples with a split ring resonator (SRR) and standard transmission/reflection waveguide methods. Then, the versatility of the designed resonator is shown in the characterization of FR4-fiberglass and Kapton polyimide samples.

The paper presents a compact patch antenna system designed using 3D printable materials and compatible with any CubeSat satellite structure. Small satellites are transforming the space industry, allowing space access with an important cost reduction for satellite industries and a shorter plan development time compared to bulky satellites. Moreover, using additive manufacturing, it is possible to design specific system components, also with a complex geometry of the inner part, without material wasting. Furthermore, a key point of 3D printing is to allow to go from design to construction straight, having an enormous effect on the supply chain. Generally, CubeSats count on Very High Frequency and Ultra High Frequency communication systems for low bit-rate uplink and downlink. Instead, S-band is among the favourite choices for high bit rates since the frequency range 2.40–2.45 GHz is one of the International Telecommunication Union (ITU) amateur satellite frequency range. An S-band printed antenna system is designed in the present paper, considering the limitations on size and the weight of CubeSat standard. The antenna system is simulated with an electromagnetic CAD, using the polylactic acid as substrate, or polylactide, a thermoplastic polyester widely used in 3D printing.

Monitoring water content in cultural heritage materials through non-invasive and easy-touse measurement systems allows the enhancement of artistic patrimony conservation activities.In this study, the response of a passive split ring resonator (SRR) used to monitor the dielectric characteristic of a material under test, when excited through an antipodal Vivaldi antenna operating close to the resonator, is analyzed through numerical simulations and then assessed by measurements.Our results reveal the possibility of monitoring the moisture content in materials largely used in artistic artifacts, such as wood and stone, through measurements of the SRR resonance frequency, which is directly related to material dielectric characteristics.

Mental disorders, including depression, pose a significant global health challenge. The rise in the prevalence of mental health issues demands innovative diagnostic tools and early intervention. This article presents a study that harnesses the power of machine learning and multi-modal data analysis to develop a robust classifier for distinguishing between healthy individuals and those with depression. The study utilizes graphological signals, such as handwriting and drawing as potential markers for depression. In this study we conducted an analysis on an existing database, upon which we developed machine learning models that outperformed existing literature. The results demonstrate the potential of these signals accurately classify individuals, with implications for early detection and telemedicine applications. Additionally, we collected new data, including handwriting, drawing, and laughter recordings, which will be utilized to create new models with the aim of achieving more effective performance. Our study also includes the integration of an inertial motion sensor into a mobile app, offering prospects for wearable technology and expanded diagnostic capabilities.

The environmental conditions and the humidity level are crucial factors in caring for artworks.The aim of this work is to propose a method for on-site non-invasive moisture monitoring of wooden artworks or structures.In this regard, a truncated open-ended coaxial probe was designed, implemented, and tested to sense (in combination with a miniaturized Vector Network Analyzer) the variations of water content in woods and stones.More in detail, for the experimental tests, two types of wood (seasoned fir and not seasoned fir) and a limestone, used in Italian Artworks and structures, were analyzed at different moisture levels.