Tunable soft pressure sensors based on magnetic coupling mediated by hyperelastic materials
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Abstract
Recent advances in pressure sensors have garnered significant interest due to their promising applications in healthcare, robotics and wearable technology. In these fields, there is an ever-increasing demand for soft sensors that can conform to complex surfaces, such as the human body. However, current sensors often face limitations in measurable pressure ranges and customization involves complex manufacturing processes. In this study, we introduce an innovative solution for producing soft pressure sensors with varying maximum detection pressures. By utilizing a magnetic transduction mechanism and different hyperelastic materials, we have developed sensors that can adapt to irregular surfaces. These sensors measure a wide range of pressures, from ultra-low to medium, and offer variable stiffness, sensitivity, and measurement ranges. The sensors we manufactured exhibit a detection range from 6.8 to 77.4 kPa, a sensitivity range between -5.1 × 10-2 and -0.4 × 10-2 kPa-1, a short recovery time of
Keywords
INTRODUCTION
In recent years, pressure sensors have attracted considerable interest due to their potential in diverse applications, ranging from healthcare to robotics. The demand for sensitive pressure sensing media is increasing significantly[1-3]. Their versatility is a major reason for this interest; for instance, when embedded in wearable chest straps, pressure sensors can measure breathing activity[4,5], or heart rate by sensing the small displacements generated by cardiac mechanics on the chest surface[6]. Additionally, these sensors are utilized in robotics for tactile sensing[7] and human motion monitoring[8], and in clinical applications, such as in anesthesiology, to detect the epidural space using the loss of resistance method[9].
The main limitation of traditional pressure sensors is their lack of flexibility, which has encouraged the scientific community to further investigate this field[10-12]. Thus, there is a growing need for tunable and flexible solutions capable of addressing different types of stimuli and accurately detecting dynamic pressure across a wide range- from ultra-low (< 1 kPa), low (1-10 kPa), medium (10-100 kPa), to high (> 100 kPa) - without compromising sensitivity[13,14]. Recent advances have introduced several innovative sensing solutions, and the concept of soft sensors has emerged[15-17], especially in human-machine and wearable electronics[18,19]. Various sensing elements have been proposed, featuring different structures and transduction mechanisms, including capacitance[20-22], piezoresistivity[23-25], and piezoelectricity[10,26,27].
Among these, piezoresistive flexible pressure sensors have been widely studied due to their simple operating principle. However, they face limitations such as low sensitivity, nonlinearity, high hysteresis, poor stability, and unsuitability for low-pressure ranges[7,23,28]. Conversely, capacitive sensors have gained significant attention due to their advantageous characteristics[29,30], including low sensitivity to temperature and humidity, low power consumption, and highly repeatable response[31]. However, they are typically suitable for high-pressure range applications[32] and are limited by poor linearity, and susceptibility to parasitic capacitance[31].
In this context, the magnetic transduction mechanism offers several advantages, such as linearity, reduced hysteresis, high repeatability and ease of fabrication[7,28,33]. This last factor is particularly noteworthy, as it contrasts with the previously discussed transduction mechanisms that typically require complex and challenging manufacturing processes, thereby complicating any modifications to their measurement range[34,35]. In this context, integrating air chambers into the deformable elements of piezoresistive or capacitive pressure sensors has shown promising potential for adjusting their stiffness[22,36-38]. However, the impact of these factors, especially their shape, on soft magnetic sensors remains unexplored. Building on these insights, we developed pressure sensors consisting of a Hall sensor and a soft element embedding a magnet. Our study was designed to enhance the tunability of soft magnetic pressure sensors through variations in the shape and material of the soft element. This methodology aims to extend the maximum detectable pressure range of these sensors, thereby broadening their applicability without necessitating changes to the fundamental magnetic and electronic architecture. We produced and metrologically characterized cylindrical-shaped sensors, implementing a Finite Element Method (FEM) model. These sensors were realized using five materials with varying stiffnesses and three different shapes: a solid cylinder, a cylindrical air chamber, and a domed air chamber. Lastly, we demonstrated the applicability of the produced sensors across various fields, including healthcare and robotics. This was achieved by recording finger-induced pressures (contact, soft touch, pressing), finger tapping, and chest deformations for non-intrusive measurement of breathing at different respiratory frequencies.
EXPERIMENTAL
Design and fabrication
We designed magnetic soft pressure sensors [Figure 1A and in Supplementary Section 1]. Each sensor comprises the following components: (i) a Hall effect sensor; (ii) a magnet (NdFeB, N45) with axial magnetization (dimensions: 1 mm height, 1 mm diameter); and (iii) a soft polymeric medium. While the Hall sensor and magnet remained unchanged, the polymeric medium varied in shape and material to modulate the sensor’s stiffness. Externally, the polymeric medium was cylindrical (height: 8 mm, diameter: 8 mm), with variations in the internal configuration: 1. lacking an inner air chamber (FULL shape);
where Vdd is the supply voltage and 20.47 mT represents the sensor’s full scale.
Figure 1. Soft magnetic pressure sensor. (A) Structure of the sensor and schematization of the operating principle; (B) Schematic representation of the mechanical behavior of the sensors under external pressures; (C) Vout and B at different distances between magnet and Hall sensor (according to Equation 1).
Metrological characterization of soft pressure sensors
To metrologically characterize each soft sensor, we measured the sensor output using a data acquisition board (DAQ NI USB-6009, National Instruments) under both quasi-static loads and loading/unloading cycles generated by a tensile testing machine (Instron 3365). We set the compressive displacement of the moving plate to 3 mm (equivalent to 37.5% compressive strain), with a displacement rate of 2 mm/min. We conducted five tests and calculated the expanded uncertainty using a t-Student distribution with four degrees of freedom and a 95% confidence level. To assess the sensor’s response to repeated compressive loading, we subjected it to ten loading and unloading cycles at four different moving plate displacement rates (0.1, 0.2, 0.8, and 1 Hz).
Materials characterization and finite element analysis of soft pressure sensors
To investigate the influence of sensor shape and materials on stiffness, we employed a FEM model. Each custom-made material underwent initial characterization under both tension and compression conditions to determine its characteristic coefficients. Compression and tensile tests were conducted following ISO 7743:2017 standards and ASTM D412:2006 guidelines. A comprehensive description of these procedures is available in Supplementary Section 2. Five specimens were fabricated according to ISO 7743:2017 and five according to ASTM D412:2006 for each material. Mechanical testing was performed using an Instron 3365 tensile testing machine, adhering to the respective standard guidelines. Stress and strain data were then input into the simulation platform (COMSOL Multiphysics, USA), where the Yeoh model was employed to determine material coefficients that best fit the experimental data. Detailed coefficients for the five materials are provided in Supplementary Table 1 in Supplementary Section 3. After characterizing the five materials, finite element analysis was conducted using the Structural Mechanics and AC/DC modules within COMSOL Multiphysics. The geometries of each soft medium (FULL shape, DOM shape, and CIL shape) were initially created using CAD software (Onshape) and subsequently imported into COMSOL Multiphysics. The Yeoh third-order hyperelastic material model and its parameters (c1, c2, and c3) were utilized to simulate the hyperelastic behavior of the five materials described previously. A displacement of 1 mm (corresponding to 12.5% deformation) was imposed on the upper surface of the silicone structure, and the resulting pressure distribution was recorded.
Breathing and finger-tapping detection
To evaluate the effectiveness of the proposed sensor structure, two sensors with varying stiffness levels were tested in different application scenarios. The study was conducted on a 35-year-old male volunteer, in accordance with the guidelines of the Declaration of Helsinki. The study received approval from the Institutional Ethics Committee of Università Campus Bio-Medico di Roma (09/19 OSS ComEt UCBM). The participant provided written consent, including permission for photography and video recording during the testing sessions, as well as consent for using recorded data for publication purposes.
In the first proof-of-concept experiment, a pilot study was conducted with a volunteer to evaluate the sensors’ capability in estimating breathing from chest wall movements. For this purpose, the sensor was integrated into a wearable band using double-sided Velcro and positioned inside the band so that the soft part of the sensor faced the subject’s chest when worn. Before the experiment, the band was worn by the volunteer with the sensor centered on the sternum at the height of the seventh rib. Subsequently, the volunteer was asked to follow the experimental protocol involving phases of uncontrolled breathing, apneas performed at the end of inhalation and exhalation, quiet breathing, and fast breathing. Each protocol phase aimed to assess the sensor’s performance across different respiratory frequencies[39-41]. Throughout each test, data from the soft sensor were collected at a sampling rate of 100 Hz using a data acquisition board and stored on a laptop. Subsequently, these data were processed using MATLAB. In the second proof-of-concept experiment, our sensor was tested in three states: no contact, soft touch, and hard touch. Additionally, three further tasks were carried out: (i) deep tapping, where the subject performed the tapping movement with major force; (ii) slow tapping; (iii) fast tapping; and (iv) tapping as quickly as possible, where the subject was asked to tap as rapidly as possible. All tasks were performed with the index finger of the dominant hand. For the tests, a PLA holder was designed to fix the soft sensor on a horizontal surface.
RESULTS AND DISCUSSION
Characterization of soft pressure sensors
In this study, the first experimental steps involved the design of the pressure sensors, the fabrication and characterization of the materials, the realization of the soft media and the assembly of the entire pressure sensors’ structure. The proposed pressure sensors exhibit a soft structure that enables control over the sensor’s stiffness, allowing the sensor to offer different measurable pressure ranges by adjusting only its shape and material composition. All soft structures were made with identical external shapes to ensure the consistency of the force application surface. However, the internal shape varied to adjust the structure’s stiffness.
Specifically, the FULL shape is the most rigid as it does not have an inner air chamber; the DOM shape has intermediate stiffness due to its structurally efficient shape that evenly distributes stresses and features a smaller inner air chamber than the CIL shape, which has the thinnest walls among the three shapes and the largest inner air chamber, resulting in lower stiffness. The materials of the soft element were also chosen to vary its stiffness. The measured compression Young’s values for each material were 81 kPa (Eco50), 71 kPa (Eco30), 47 kPa (Eco30t10), 41 kPa (Eco30t20), and 34 kPa (Eco30t25). Additional information is provided in Supplementary Section 4.
The proposed sensors measure pressure through variations in the distance between the Hall sensor and the magnet. When a force is applied to the surface of the polymeric medium containing the magnet, the medium deforms by an amount that depends on its stiffness (determined by its shape and material). As the magnet moves, the magnetic field interacts with the Hall sensor, causing a change in its output voltage. The distance of the magnet from the Hall sensor determines the measured output voltage, which can be translated into the pressure applied to the soft medium. This is the foundation of the pressure sensor’s sensing mechanism.
The component of the magnetic field perpendicular to the Hall sensor generates a measurable Hall voltage expressed with:
where I represents the current flowing through the sensor, B indicates the magnetic flux density, n stands for the density of mobile charges, e is the charge of an electron and t points to the thickness of the sheet of conductive material composing the sensor itself.
To metrologically characterize the sensors, both static and dynamic tests were conducted. From the graph in Figure 2A, it can be observed that the measurable pressure range increases with the stiffness of the soft media, and that the mechanical response is linear throughout the deformation range for the FULL and DOM shapes and for all materials. This is not the case for the CIL shape; however, the mechanical response is linear in the 0%-12.5% deformation range for all shapes and materials. Additionally, the sensor calibration curve [Figure 2B] shows the influence of the shape and material on the maximum pressure value measured under the same deformation. In the initial state, with zero deformation of the soft medium, the sensor output is equal to Vdd/2, indicating its saturation point when the magnet is positioned 2 mm from the Hall sensor. Figure 2C illustrates the voltage measured by the Hall sensor as the soft medium undergoes deformation across various shapes and materials.
This data highlights the sensors’ reproducibility, independent of the differences in material composition and shape. The sensitivity values were calculated in the range 0%-37.5% compressive strain as the normalized values of voltage variations (
Figure 2. (A) Change in pressure and force applied to the upper surface of the outer cylinder of the soft sensors as a function of deformation for all the shapes and materials of the soft medium. Solid line: average value; filled area: uncertainty; (B) Change in voltage measured by the magnetic pressure sensors as a function of pressure applied to the upper surface for all the shapes and materials of the soft medium. Solid line: average value; filled area: uncertainty; (C) Change in voltage measured by the magnetic pressure sensors as a function of deformation. Solid line: average value; filled area: uncertainty; (D) Voltage variation of the soft pressure sensor with CIL shape and Eco30 after 1,000 loading/unloading cycles at 0.8 Hz.
Overall, the softer materials show higher sensitivity values than the stiffer ones. As shown in Table 1, these values are higher for the softer shapes (max value: CIL shape with Eco30t25, -5.1 × 10-2 kPa-1) while they are lower for the stiffer shapes (min value: FULL shape with Eco50, -0.4 × 10-2 kPa-1).
| Eco50 | Eco30 | Eco30t10 | Eco30t20 | Eco30t25 | |
| FULL shape | |||||
| -0.4 × 10-2 | -0.6 × 10-2 | -0.8 × 10-2 | -0.8 × 10-2 | -1.2 × 10-2 | |
| Recovery time [s] | 0.40 | 0.42 | 0.41 | 0.43 | 0.41 |
| DOM shape | |||||
| -0.8 × 10-2 | -1.2 × 10-2 | -1.5 × 10-2 | -2.3 × 10-2 | -2.5 × 10-2 | |
| Recovery time [s] | 0.42 | 0.41 | 0.41 | 0.44 | 0.42 |
| CIL shape | |||||
| -1.1 × 10-2 | -2.4 × 10-2 | -2.5 × 10-2 | -4.9 × 10-2 | -5.1 × 10-2 | |
| Recovery time [s] | 0.46 | 0.44 | 0.39 | 0.44 | 0.40 |
These sensitivity values show considerable promise, surpassing those typically achieved by pressure sensors utilizing alternative principles such as piezoresistive technology[42-44]. Furthermore, these sensors exhibit excellent sensitivity across a broad range of measurable pressures, comparing favorably with existing literature findings[31]. Recovery time appears to be independent of both the shape and the material of the soft sensor element. Consequently, it is also independent of the measured pressure range, being approximately 0.4 s for all the sensors [Table 1 and Supplementary Section 5]. This demonstrates the sensors' ability to return to their initial shape after pressure application, making them suitable for many applications requiring measurement of cyclic phenomena. The results are very interesting when compared to sensors based on other operating principles[45-47].
The sensors’ output was also evaluated under cyclic loading and unloading cycles at different compression rates (0.1, 0.2, 0.8, and 1 Hz at 25% compression strain) to test their behavior across a wide range of frequencies. The hysteresis error values depend mainly on the compression rate, with no significant difference when varying the shape and material [Table 2]. Specifically, the sensors exhibit higher hysteresis values as the frequency of the cyclic load increases. They range from 2%-6% for compression rates up to
Hysteresis values obtained per each material and each shape at different compression rates
| Eco50 | Eco30 | Eco30t10 | Eco30t20 | Eco30t25 | |
| FULL shape | |||||
| 0.1 Hz | 2.74% | 2.94% | 3.89% | 3.03% | 3.20% |
| 0.2 Hz | 4.89% | 5.77% | 3.18% | 3.06% | 5.57% |
| 0.8 Hz | 7.46% | 6.79% | 9.39% | 6.90% | 8.87% |
| 1 Hz | 10.68% | 9.02% | 8.76% | 11.58% | 9.64% |
| DOM shape | |||||
| 0.1 Hz | 2.69% | 3.63% | 3.63% | 3.24% | 4.23% |
| 0.2 Hz | 4.98% | 6.23% | 5.12% | 4.44% | 4.72% |
| 0.8 Hz | 8.45% | 9.59% | 7.50% | 9.14% | 6.74% |
| 1 Hz | 9.89% | 12.66% | 15.26% | 11.69% | 12.18% |
| CIL shape | |||||
| 0.1 Hz | 3.14% | 3.59% | 4.04% | 2.65% | 3.14% |
| 0.2 Hz | 5.89% | 5.93% | 4.20% | 4.51% | 4.34% |
| 0.8 Hz | 9.86% | 8.98% | 7.99% | 10.10% | 9.70% |
| 1 Hz | 11.60% | 10.36% | 8.66% | 13.60% | 10.01% |
FEM for soft pressure sensors
To analyze the impact of the shape and materials on the sensor’s mechanical behavior, a FEM was implemented. The materials used for the soft medium were characterized according to respective standard guidelines. Stress and strain data were calculated [Figure 3A], and then input into the simulation platform, where the Yeoh model determined material coefficients that best fit the experimental data. Subsequently, simulations were conducted considering each sensor’s shape and material to study their mechanical behavior [Figure 3B]. To assess the validity of the simulations, a comparison was carried out between the maximum detection limit of pressure values obtained from the FEM and the experimental results on all the samples at 12.5% compression of the soft medium [Figure 3C]. A reasonable discrepancy was found between the simulated and experimental data [Figure 3D], which increases with the sensor’s stiffness. However, both simulated and experimental data show an increasing maximum detection limit as the material stiffness increases and the inner chamber size within the soft medium decreases. The simulated pressure values are in the range from 3.29 kPa (Eco30t25) to 8.50 kPa (Eco50) for the CIL shape; range from 4.12 kPa (Eco30t25) to 10.64 kPa (Eco50) for the DOM shape; and range from 5.63 kPa (Eco30t25) to
Figure 3. (A) Stress-strain curve obtained for the five materials tested as a result of the combination of tensile and compression tests; (B) Von Mises stress obtained from the FEM simulations for one shape and each material of the soft medium of the magnetic pressure sensors; (C) pressure change measured on the upper surface of the sensors during the experimental tests (solid line) and during the FEM simulations (dashed line) for the different materials and shapes; (D) Maximum pressure detection limit measured during the experimental tests (solid color) with the related uncertainty (black error bar) and during the simulations (color with transparency) for each shape and material of the soft medium. M1: Eco50; M2:Eco30; M3:Eco30t10; M4:Eco30t20; M5:Eco30t25. FEM: Finite Element Method.
Breathing and finger-tapping detection through soft pressure sensors
To demonstrate the sensors’ effectiveness and adaptability to different applications, they were used for physiological parameter monitoring and tactile sensing. Two proof-of-concept experiments were carried out. The first test involved using the soft sensors for respiratory monitoring, providing critical information on a patient’s breathing patterns and overall respiratory health. Breathing frequency can also provide useful information about a subject's psychophysical state. Accurate measurement of various respiratory rates and detection of apnea is essential for diagnosing and managing conditions such as obstructive sleep apnea[51,52], helping to prevent complications, ensuring timely interventions[53], and improving patient outcomes[54] and athletes training[55,56]. The sensor was integrated into a wearable chest strap for breathing monitoring [Figure 4A]. We measured the pressure exerted by the sternum while breathing. The sensor provided a pattern where each respiratory cycle (inhalation-exhalation) had an increasing phase, a maximum peak, and a decreasing phase. Pilot experiments were carried out on a healthy volunteer (male, 35 years old) to investigate the sensors’ ability to estimate breathing from chest wall movements. The rib cage movements during breathing apply force to the soft medium of the sensors, causing magnet displacement and a change in the Hall sensor output. For this purpose, the CIL shape Eco30 sensor was embedded into a wearable band using double-sided Velcro. This sensor was selected for its balance between sensitivity and hysteresis, relevant for respiratory monitoring. The soft part of the sensor was positioned toward the subject’s chest, centered on the sternum at the height of the seventh rib.
Figure 4. (A) Representation of the experimental setup including the CIL shape Eco30 sensor placed in a chest strap worn by the subject; (B) Respiratory signal collected during the breathing test showing the different phases of the protocol including slow and fast breathing and apnea phases; (C) Experimental setup used during the tapping test; (D) Signal collected during the tapping test, in the absence of touching the sensor with the finger, when a soft touch is applied and when a hard touch is applied; (E) Signal collected during the tapping test which includes deep tapping, slow tapping fast tapping and tapping as fast as possible.
The volunteer followed an experimental protocol that included different breathing rates and depths. All protocol steps were performed to investigate the functionality of the pressure sensors at different respiratory frequencies within the physiological range. As shown in Figure 4B, the data collected with the proposed sensor allow the identification of slow breathing, and fast breathing phases. The apnea phases are also identifiable in the signals, shown by an almost constant signal throughout the apnea duration. End-inspiratory and end-expiratory apnea can be distinguished by their different mean values. This feature could be crucial for devices studying apnea, not found in many respiratory monitoring devices[57]. The minimum thickness achieved in this real-life application is 6.1 mm.
The second proof-of-concept experiment measured tapping, useful in numerous applications including the evaluation of muscle control or the assessment of patient recovery after a stroke[58]. Movements are typically monitored in amplitude, rhythm, and speed to assess disorders such as muscle tremor and rigidity[59].
We measured the pressure exerted on the sensor by the impact of the finger at different speeds and intensities. Specifically, our sensor was tested under three conditions: no contact, soft touch, and hard touch [Figure 4C]. Figure 4D shows the output voltage in soft touch, hard touch and no touch phases, easily distinguishable. Then, the sensor was evaluated during four additional tasks: deep tapping with maximum force, slow tapping, fast tapping, and tapping as fast as possible. Figure 4E shows the sensor output at different tapping frequencies. Regardless of the tapping frequency, the sensor always returns to its initial value in the absence of pressure, showing good repeatability. The minimum thickness achieved in this real-life application is 5.6 mm.
The results from these tests demonstrate the sensors’ ability to follow both slow (e.g., touch and breath during apnea and quiet breathing) and rapid phenomena (e.g., tapping or breathing during tachypnoea).
CONCLUSIONS
This study introduces a novel approach to designing and fabricating soft pressure sensors with tunable stiffness, offering adaptability across various applications. We developed and characterized sensors capable of detecting medium and low-pressure ranges. Our sensor employs a magnetic sensing mechanism, where deformation of a soft polymeric medium induces changes in magnetic field intensity, allowing for measurement of applied pressure.
Our findings reveal significant variations in sensor performance based on shape and material composition. Sensitivity values range from 0.4 × 10-2 to 5.1 × 10-2 kPa-1 across different configurations. Moreover, the sensors exhibit a wide range of maximum detectable pressure values, up to 77.4 kPa, depending on the stiffness of the material and the sensor’s shape. Experimental tests demonstrated the sensor’s robust performance under both static and dynamic loading conditions. The sensors also show a promising recovery time of 0.4 s, making it suitable for real-time pressure monitoring. Table 3 compares our sensor with similar sensor technologies.
Comparison of pressure sensors
| Sensing area | Working principle | Sensitivity | Recovery time | Measuring range | Ref. |
| 15 × 15 mm2 | Capacitive | 0.161 kPa-1 | N.D. | 0-10 kPa | [4] |
| 6 × 6 mm2 | Capacitive | 0.000022 kPa-1 | N.D. | 240-1,000 kPa | [32] |
| N.D. | Capacitive | 0.01 kPa-1 | 800 ms | 18-70 kPa | [22] |
| 10 × 10 mm2 | Piezoresistive | 0.096 kPa-1 | N.D. | 0-175 kPa | [60] |
| 28 × 28 mm2 | Piezoresistive | 0.011 kPa-1 | N.D. | 0-120 kPa | [24] |
| 56 mm2 | Magnetic | 16 mV/N | N.D. | 0-0.05 kPa | [33] |
| 50 mm2 | Magnetic | -0.051 kPa-1 | 400 ms | 0-7 kPa | Our work: CIL, Eco30t25 |
| 50 mm2 | Magnetic | -0.004 kPa-1 | 400 ms | 0-80 kPa | Our work: FULL Eco50 |
We tested the sensors in two different application fields to demonstrate their versatility. One sensor was tested for respiratory monitoring during normal breathing and high-frequency breathing (tachypnoea). It demonstrated the sensor’s ability to follow rapid and minimal changes well. An important feature of our sensors is their monoaxial design, which primarily detects magnet movements perpendicular to the Hall sensor (z-axis). This design is expected to reduce susceptibility to motion artifacts during activities such as walking, where magnet movements predominantly occur in the xy-plane.
Future research will involve comprehensive testing to assess the sensor’s performance and susceptibility to motion artifacts in real-life applications. This includes analyzing its behavior during various activities and comparing it with other sensor types to establish robustness and reliability. Another sensor was tested during tapping at different frequencies and intensities, demonstrating strong adaptability to various operating conditions. Overall, the sensors presented in this work demonstrate significant potential for miniaturization and customization to specific applications. The dimensions of the current design can be reduced by optimizing the geometry, materials, and magnetization of the sensor components. This miniaturization will enhance user comfort and expand its applicability in wearable and robotic devices.
DECLARATIONS
Authors’ contributions
Conceived the concept of the magnetic pressure sensors tunable by hyperplastic materials mediums and supervised the project: Massaroni C
Designed the pressure sensors and conducted all the experiments: Romano C, Lo Presti D, Massaroni C
Provided support throughout the experiment process and revised the manuscript: Silvestri S, Schena E
Contributed to discussions regarding the performance and analysis of the architected pressure sensors: Romano C, Lo Presti D, Massaroni C
Participated in writing: Romano C, Massaroni C
Availability of data and materials
The datasets, including experimental data and simulation models presented in this study, and the code used and presented in this study are available upon request from the corresponding author.
Financial support and sponsorship
The authors acknowledge the financial support from European Union-Next Generation EU-NRRP M4.C2-Investment 1.5 Establishing and Strengthening of Innovation Ecosystems for Sustainability (Rome Technopole) under Project ECS00000024.
Conflicts of interest
Massaroni C is the guest editor of the special issue, while the other authors have declared that they have no conflicts of interest.
Ethical approval and consent to participate
This study was conducted in compliance with the Declaration of Helsinki’s guidelines and received approval from the Università Campus Bio-Medico di Roma’s Institutional Ethics Committee (09/19 OSS ComEt UCBM). All participants were informed about the experimental procedure and research purposes, and have signed the relevant consent forms prior to participation.
Consent for publication
Participants have been informed that their detailed information and images will be used for the purpose of this study. They have also signed written consent forms allowing those details to be published.
Copyright
© The Author(s) 2024.
Supplementary Materials
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, ... Carlo MassaroniHow to Cite
Romano, C.; Presti D. L.; Silvestri S.; Schena E.; Massaroni C. Tunable soft pressure sensors based on magnetic coupling mediated by hyperelastic materials. Soft. Sci. 2024, 4, 31. http://dx.doi.org/10.20517/ss.2024.24
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