Pressure Sensor Research Articles

Self-Powered Pressure Sensor Based on Flexible Permanent Magnet Material

Self-Powered Pressure Sensor Based on Flexible Permanent Magnet Material

Research of Long-Term Stability of High-Sensitivity Piezoresistive Pressure Sensors for Ultralow Differential Pressures

Research of Long-Term Stability of High-Sensitivity Piezoresistive Pressure Sensors for Ultralow Differential Pressures

Highly durable MXene-based pressure sensor enabled by homogeneous interfacial locking

Pressure sensors with linear high sensitivity and a broad response range enable the capture of subtle pressure changes across various scenarios, making them essential for accurate detection. Conventional methods typically employ porous and layered designs to boost sensitivity but they can compromise stability due to mechanical mismatches and interface incompatibilities between diverse materials. To address these issues, we propose a homogeneous interfacial locking strategy that simultaneously improves the linear response range and stability of the sensor. This approach uses aqueous polyurethane (WPU) as the interface-locking material, which matches the material properties of the substrate polyurethane (PU) sponge. This compatibility anchors the deposited MXene nanosheets to the sponge framework, establishing a robust mechanical and electrical interface. Additionally, the partial filling of the sponge’s pores with WPU enhances its mechanical compressibility. As a result, the sensor achieves high sensitivity (6226 kPa−1), a wide detection range (up to 146 kPa) and excellent durability (over 100,000 cycles), making it suitable for long-term human posture monitoring and analysis. This strategy offers a promising pathway for developing pressure sensors with high sensing performance and improved stability.

Design Rules for 3D Printing‐Assisted Pressure Sensor Manufacturing: Achieving Broad Pressure Range Linearity

AbstractRecent advancements in 3D printing technology have expanded its application to manufacturing pressure sensors. By harnessing the cost‐effectiveness, streamlined processes, and design flexibility of 3D printing, sensor fabrication can be customized to meet specific performance needs. Thus far, 3D printing in pressure sensor development has been primarily limited to creating molds for transferring patterns onto flexible substrates, restricting both material selection and sensor performance. To fully unlock the potential of 3D printing in advanced pressure sensor fabrication, it is crucial to establish effective design rules focused on enhancing the figure of merit performance. This study introduces a universal design strategy aimed at maintaining high sensitivity across a wide pressure range—a challenging feat, as sensitivity significantly decreases at higher pressures. Our approach centers on engineering the deformability of 3D‐printed structures, achieving a linear increase in contact area between sensor patterns and electrodes without reaching saturation. Sensors designed with high elongation and low stiffness exhibit consistent sensitivity of 162.5 kPa⁻¹ across a broad pressure range (0.05–300 kPa). Mechanistic investigations through finite element analysis confirm that engineered deformability is key to achieving this enhanced linear response, offering robust sensing capabilities for demanding applications such as deep‐sea and space exploration.

Multi-Dimensionally Functionalized Pressure Sensor for Human Motion Detection Based on 1D AgNWs/2D rGO-Coated 3D Porous Sponge.

This study presents a conductive-type pressure sensor based on a conductive composite of 1D/2D nanomaterials coated onto a 3D nonconductive polymer structure with various pores. A 3D porous elastomer for the substrate was fabricated by using a sugar template, which led to an increased mechanical deformation range. The sugar template enhanced the surface roughness of the polymer, resulting in an improvement in the adhesion of nanomaterials to the polymer surface. Subsequently, it was functionalized by coating with hybrid nanomaterials of 1D silver nanowires (AgNWs) and 2D reduced graphene oxide (rGO) through a dip-coating process. When pressure is applied, the rGO/AgNWs/ecoflex pressure sensor deforms along the direction of the applied force, causing the conductive multidimensional nanomaterials to come into contact. Consequently, the improved networks between the two nanomaterials expanded the current paths, increasing the current detected through the electrodes attached to the sensor. The rGO/AgNWs/ecoflex pressure sensor, with its porous structure within the flexible ecoflex, demonstrated a high sensitivity (up to 2.29 kPa-1) over a wide detection range of 0-120 kPa. This enables the monitoring of a wide range of motions, including small pressures such as subtle touch, respiratory vibrations, and drinking, as well as large pressures such as human bodily movements, finger/arm bending, and foot pressure, making it an excellent candidate for applications requiring precise pressure detection.

Minimalist Design for Multi-Dimensional Pressure-Sensing and Feedback Glove with Variable Perception Communication

Immersive human–machine interaction relies on comprehensive sensing and feedback systems, which enable transmission of multiple pieces of information. However, the integration of increasing numbers of feedback actuators and sensors causes a severe issue in terms of system complexity. In this work, we propose a pressure-sensing and feedback glove that enables multi-dimensional pressure sensing and feedback with a minimalist design of the functional units. The proposed glove consists of modular strain and pressure sensors based on films of liquid metal microchannels and coin vibrators. Strain sensors located at the finger joints can simultaneously project the bending motion of the individual joint into the virtual space or robotic hand. For subsequent tactile interactions, the design of two symmetrically distributed pressure sensors and vibrators at the fingertips possesses capabilities for multi-directional pressure sensing and feedback by evaluating the relationship of the signal variations between two sensors and tuning the feedback intensities of two vibrators. Consequently, both dynamic and static multi-dimensional pressure communication can be realized, and the vibrational actuation can be monitored by a liquid-metal-based sensor via a triboelectric sensing mechanism. A demonstration of object interaction indicates that the proposed glove can effectively detect dynamic force in varied directions at the fingertip while offering the reconstruction of a similar perception via the haptic feedback function. This device introduces an approach that adopts a minimalist design to achieve a multi-functional system, and it can benefit commercial applications in a more cost-effective way.

Sheet-laminated additive manufacturing of bacterial cellulose nanofiber-reinforced hydrogels

Three-dimensional (3D) printing of hydrogels offers promising potential for creating intricate, customizable structures with superior elasticity, softness, and biocompatibility. However, due to their high-water content, hydrogels often suffer from reduced mechanical strength, which is further decreased when they absorb water, limiting their use in environments requiring high mechanical durability. To address this, we developed a novel 3D printing technique to fabricate bacterial cellulose (BC) nanofiber-reinforced hydrogels, which we term sheet-laminated additive manufacturing (SLAM). SLAM is based on digital light processing (DLP) 3D printing technology and involves a process of sequentially layering BC nanofiber sheets impregnated with a photocrosslinkable monomer. The BC nanofiber sheets provide a unique 3D network, resulting in a significant enhancement of the mechanical strength of various photocrosslinkable hydrogels. A unique aspect of BC sheets is their ability to further improve mechanical properties by inducing nanofiber alignment or adjusting nanofiber density through stretching and compression pretreatments. The printed BC nanofiber-reinforced hydrogels maintained their strength after swelling and demonstrated exceptional performance in applications requiring high mechanical robustness. Our SLAM approach successfully created complex 3D structures, such as BC-reinforced hydrogel earthworm structures and pressure sensors, demonstrating its potential for advanced applications in high-stress environments.

Fall Detection in Q-eBall: Enhancing Gameplay Through Sensor-Based Solutions

The field of physically interactive electronic games is rapidly evolving, driven by the fact that it combines the benefits of physical activities and the attractiveness of electronic games, as well as advancements in sensor technologies. In this paper, a new game was introduced, which is a special version of Bubble Soccer, which we named Q-eBall. It creates a dynamic and engaging experience by combining simulation and physical interactions. Q-eBall is equipped with a fall detection system, which uses an embedded electronic circuit integrated with an accelerometer, a gyroscopic, and a pressure sensor. An evaluation of the performance of the fall detection system in Q-eBall is presented, exploring its technical details and showing its performance. The system captures the data of players’ movement in real-time and transmits it to the game controller, which can accurately identify when a player falls. The automated fall detection process enables the game to take the required actions, such as transferring possession of the visual ball or applying fouls, without the need for manual intervention. Offline experiments were conducted to assess the performance of four machine learning models, which were K-Nearest Neighbors (KNNs), Support Vector Machine (SVM), Random Forest (RF), and Long Short-Term Memory (LSTM), for falls detection. The results showed that the inclusion of pressure sensor data significantly improved the performance of all models, with the SVM and LSTM models reaching 100% on all metrics (accuracy, precision, recall, and F1-score). To validate the offline results, a real-time experiment was performed using the pre-trained SVM model, which successfully recorded all 150 falls without any false positives or false negatives. These findings prove the reliability and effectiveness of the Q-eBall fall detection system in real time.

MEMS capacitive pressure sensor: analysis, theoretical modeling, simulation and performance comparison of the effect of a conical notch

Abstract Micro-Electro-Mechanical Systems (MEMS) pressure sensors are widely used in various applications due to their high sensitivity, low power consumption, and compactness. This work involves the design and simulation of a MEMS-based Touch Mode Capacitive Pressure Sensor (TMCPS). The proposed sensor is based on a substrate with an integrated conical notch featuring a circular diaphragm, aiming to enhance the key performance parameters of the sensor. The integration of a conical notch in the substrate increases the touch area between the diaphragm and substrate compared to the literature, ensuring increased capacitance and capacitive sensitivity. In this work the mathematical analysis of deflection of a circular diaphragm employing thin plate theory, capacitance, and capacitive sensitivity, along with step-by-step explanations, is provided. The results are obtained from MATLAB simulations. The deflection of the diaphragm is validated through Finite Element Analysis (FEA) using COMSOL Multiphysics. The proposed work demonstrates a significant improvement in sensor sensitivity compared to the existing literature.

LC dynamic signal modeling and analysis of resonant frequency measurement error under rapid eye movements interference

Abstract LC resonant wireless passive intraocular pressure (IOP) sensors measure IOPs by mapping them to LC resonance frequencies. However, during the sweeping acquisition process of each LC signal frame, possible rapid eye movement (REM) can interfere with the wireless mutual inductance coupling, leading to the signal distortion and resulting in the error of resonance frequency measuring. Currently, there is a lack of modelling analysis of the errors generated by REM effect, resulting in an absence of guidance for reducing the impact. Therefore, here, we start from the perspective of the signal model and establish the LC dynamic signal model for sweeping acquisition. By means of the limit state analysis and Monte Carlo simulation, we analyze the influence of external REM parameters (including REM range and velocity) and internal parameters of the LC sensing system (including the quality coefficient of LC sensor, diameter, and number of turns of the reader coil, signal sweep acquisition speed and period) on the errors. We demonstrated theoretical relationship between the extreme errors with these parameters and verify it through a computer simulation. Based on the results, we propose to optimize the internal parameters of the LC sensing system to reduce the REMs effect on errors, safeguarding the quality of signal acquisition and improving the measurement accuracy.

IoT‐Based Hand Hygiene Compliance Monitoring System and Validation of Its Effectiveness in Hospital Environments

AbstractHealthcare‐associated infection (HAI) is the most common adverse medical event that affects patients. Internationally, healthcare workers (HCWs) are monitored for hand hygiene compliance to reduce HAI risk. While direct observation is considered the gold standard for monitoring, it has several disadvantages. To address this, the study focused on developing a comprehensive hand hygiene system that integrates the Internet of Things (IoT) hand hygiene with soap and water (HHW) and alcohol‐based formulation (HHA) monitoring, incorporates real‐time data visualization on a web interface to track HCWs' hand hygiene practices, and provides instant calculations of compliance and accuracy rates. This IoT system uses Bluetooth for HCW positioning and HHW detection, ultrasonic sensors for handwashing duration, and pressure sensors for HHA detection. Furthermore, a cloud server, database, and website are established to manage and display the data received by the IoT devices. To reduce HAI in Taiwan, hospitals must provide both HHW and HHA systems, and HCWs can choose either method when hand hygiene is necessary. The system achieved 72% accuracy in clinical practice within an adult intensive care unit (ICU).

Wearable System Integrating Dual Piezoresistive and Photoplethysmography Sensors for Simultaneous Pulse Wave Monitoring.

Wearable, flexible piezoresistive pressure sensors have garnered substantial interest due to their diverse applications in fields such as electronic skin, robotic limbs, and cardiovascular monitoring. Among these applications, arterial full pulse waveform monitoring stands out as a critical area of research. The emergence of piezoresistive pressure sensors as a prominent tool for capturing pulse waveforms has led to extensive investigations. However, the lack of a linear response while achieving high sensitivity, limited compactness of signal collection systems, and scaling issues are a few potential causes of the gap between research advances and technology market readiness. Here, we present a scalable dual piezoresistive sensor that uses two complementary resistance-changing mechanisms to balance the trade-off between linear response and high sensitivity. This synergic design enables excellent sensitivity of 8.4 kPa-1 and near linear pressure response. It boasts a fast response time of 95/145 ms for loading and unloading at a pressure of 10.1 kPa. The durability tests, encompassing nearly 5000 two-stage compression cycles, validate the reliable performance of the sensor, even after prolonged use. Leveraging these performance metrics, we developed a high-resolution and compact signal collection device capable of accurately detecting the three distinct peaks associated with the full pulse waveform, including the systolic and reflected diastolic peaks. Moreover, the signal acquisition system incorporates a photoplethysmography sensor, which, when paired with the dual piezoresistive sensor, offers new insights into localized vascular health monitoring. The unique feature of our approach is its ability to perform localized vascular monitoring using multiple sensors placed on different veins. The simultaneous detection of forward- and backward-going pulse waves at the body extremities allows for a comprehensive evaluation of localized vascular health, which is not achievable with a single sensor. Finally, the comparison of wrist pulse waveforms between the compact signal collection device and a standard sourcemeter (such as the Keithley 2460) confirmed that the wearable sensing system is on par with conventional sourcemeters in terms of capturing the typical details of the full pulse waveform (i.e., systolic peak, dicrotic notch, and diastolic peak). This validation underscores the reliability and effectiveness of the developed wearable sensing system for practical and continuous pulse waveform monitoring.

Visual Piezoresistive Dual‐Response Sensor Based on CaZnOS: Mn Mechanoluminescence Materials

AbstractThe ability to achieve real‐time movement visualization in piezoresistive sensors remains a challenge. A visual piezoresistive sensor to perceive the intensity of mechanical stimuli and visible spatial location information is designed based on the mechanoluminescence material CaZnOS: Mn. The linearity, detection range, sensitivity, and stability of the sensor are tested, and the sensing mechanism of the sensor is discussed, and the relationship between force, resistance, and light intensity is established. A 5 × 5 sensor array is prepared to realize the visual detection of dynamic force trajectory, and combined with a convolutional neural network and random forest algorithm, the human writing number and pressure characteristics are recognized, and the writing path and handwriting of the robot arm are controlled by multi‐feature input. The experimental results show that the machine learning algorithm is very reliable with an accuracy of 98.33% for digit recognition and 97.21% for identity recognition. The visual piezoresistive pressure sensor provides a new idea for the visualization of flexible pressure and promotes the development of flexible sensors towards integration and intelligence.

The Prediction of the Leakage Airflow Rate Using the Supply and Return Airflow Rate in a Variable Air Volume System

Pressure differences in the envelope of a building result in leakage airflow (i.e., the unintended flow of air). This can lead to increased building heating and cooling energy, decreased thermal comfort for occupants, and the spread of moisture. To address this problem, it is necessary to know the leakage airflow in a building. Generally, the leakage airflow in a building is calculated by determining the leakage function through fan pressurization methods, such as the blower door test, and substituting the pressure difference measured by the pressure sensor. However, it is difficult to install continuous pressure sensors in an operating building. Therefore, this study proposes a method to utilize the supply and return airflow of an air conditioning system to predict the variation in the leakage airflow with changing indoor and outdoor airflow, and the efficacy of this approach was verified through experiments. The experiment measured the indoor and outdoor pressure difference of the building with a change in the speed of the supply and return fans and the opening rate of the variable air volume (VAV) damper. As a result of the experiment, the indoor–outdoor pressure difference is proportional to the difference between the indoor supply airflow and the ventilation airflow. In addition, the relationship between the pressure difference and the leakage airflow was derived through the pressurization/decompression method using an air handler, and the leakage airflow from the pressure difference generated by the operation of the air conditioning system was calculated. Lastly, the relationship between the supply and return airflow difference and the leakage airflow was derived based on the experimental results, and the leakage airflow was predicted based on the relationship.

Real-Time Tracking System for City Bus Location And Passenger Count

This survey paper reviews the advancements in real-time tracking and passenger counting systems for city buses, emphasizing the growing need for efficient, data-driven public transit solutions. As urban populations rise, city transport systems face challenges in managing demand, reducing congestion, and improving service reliability. Real-time tracking systems using GPS, GIS, and IoT-enabled sensors enable passengers and transit authorities to monitor bus locations and optimize travel times. Additionally, passenger counting systems—utilizing infrared, AI-based vision models, and pressure sensors—provide data on bus occupancy levels, allowing commuters to make informed travel decisions and helping transit agencies manage resources efficiently. This paper examines existing technologies, compares various tracking and counting methods based on accuracy, cost, and scalability, and explores recent trends in artificial intelligence, big data analytics, and smart city integration. Furthermore, it identifies critical challenges, including data privacy, accuracy in urban environments, and energy consumption, proposing future research directions for enhancing the reliability, sustainability, and user-friendliness of these systems. Our findings underscore the transformative potential of real-time bus tracking and passenger counting in advancing public transportation infrastructure and enhancing urban mobility

Stretchable and High-Performance Fibrous Sensors Based on Ionic Capacitive Sensing for Wearable Healthcare Monitoring.

Electronic textiles with remarkable breathability, lightweight, and comfort hold great potential in wearable technologies and smart human-machine interfaces. Ionic capacitive sensors, leveraging the advantages of the electric double layer, offer higher sensitivity compared to traditional capacitive sensors. Current research on wearable ion-capacitive sensors has focused mainly on two-dimensional (2D) or three-dimensional (3D) device architectures, which show substantial challenges for direct integration with textiles and compromise their wearing experience on conformability and permeability. One-dimensional (1D) stretchable fiber materials serve as vital components in constructing electronic textiles, allowing for rich structural design, patterning, and device integration through mature textile techniques. Here, a stretchable functional fiber with robust mechanical and electrical performances is fabricated based on semi-solid metal and ionic polymer, which provided a high stretchability and good electrical conductivity, enabling seamless integration with textiles. Consequently, high-performance stretchable fiber sensors are developed through different device architecture designs, including pressure sensors with high sensitivity (7.21kPa-1), fast response (60ms/30ms), and excellent stability, as well as strain sensors with high sensitivity (GF = 1.05), wide detection range (0-300% strain), and excellent sensing stability under dynamic deformations.

Aluminium surface work hardening enables multi-scale 3D lithography.

Multi-scale structures are ubiquitous in biological systems. However, manufacturing man-made structures with controllable features spanning multiple length scales, particularly down to nanoscale features, is very challenging, which seriously impacts their collective properties. Here we introduce an aluminium-based three-dimensional lithography that combines sequential nano-micro-macro-imprinting and anodization of multi-scale anodic aluminium oxide templates to manufacture well-defined multi-scale structures, using various materials. The high-fidelity nano-patterns and micro-patterns were facilitated by the surface work hardening phenomenon, where the nano-patterns can be further fine-tailored by anodization to have high-aspect-ratio and tunable nano-holes. Based on the aluminium-based three-dimensional lithography, multi-scale materials across length scales of at least 107 orders of magnitude were precisely fabricated, including carbon, semiconductors and metals. We integrated pressure sensors and biosensors with superior and customizable performances by tailoring the multi-scale carbon networks on different length scales from nanofibres and micropyramids to macrodome arrays. This work provides a versatile technique for prototyping on-demand multi-scale structures and materials to explore desirable mechanical and physical properties.

Balance Ability Characteristics and Related Factors in Athletes Across Different Sports: A Preliminary Study.

We conducted a cross-sectional study to examine two-leg- and one-leg-type balance characteristics in athletes and explore factors related to their balance ability. A total of 213 participants, including athletes from various sports (gymnastics, boat racing, swimming, soccer, judo, and baseball) and non-athletes, were included (142 men, 71 women, average age 21.5 ± 2.1 years). Balance ability was classified into two-leg and one-leg types using the modified index of postural stability (mIPS) in a two-leg stance and the one-legged stance duration with eyes closed (OLS). Body composition, upper and lower limb strength, and lower limb sensation were also measured. To examine the balance characteristics of each sport, the mIPS and OLS were used as dependent variables in a multiple regression model with age, height, weight, and sex as independent variables. The results showed a significantly higher mIPS in gymnastics (estimate: 0.22) and boat racing (0.14), and it was lower in swimming (-0.25). The OLS was significantly higher in soccer (16.98), judo (16.23), gymnastics (9.77), and baseball (9.12) and significantly lower in swimming (7.93). Additionally, the mIPS was independently associated with knee extension strength (0.12), sensory motor variables (-0.004), and height (-0.01). The OLS was associated with skeletal muscle mass (1.85) and height (-1.42). In summary, gymnasts showed superior two-leg and one-leg balance; boat racers excelled in two-leg balance; swimmers showed inferior two-leg but better one-leg balance; and soccer, judo, and baseball athletes demonstrated superior one-leg balance. Additionally, the mIPS was associated with knee extensor strength, plantar pressure sensation, and height, whereas the OLS was associated with skeletal muscle mass and height.

Stress-induced artificial neuron spiking in diffusive memristors

Diffusive memristors owing to their ability to produce current spiking when a constant or slowly changing voltage is applied are competitive candidates for development of artificial electronic neurons. These artificial neurons can be integrated into various prospective autonomous and robotic systems as sensors, e.g. ones implementing object grasping and classification. We report here Ag nanoparticle-based diffusive memristor prepared on a flexible polyethylene terephthalate substrate in which the electric spiking behaviour was induced by the electric voltage under an additional stimulus of external mechanical impact. By changing the magnitude and frequency of the mechanical impact, we are able to manipulate the spiking response of our artificial neuron. This functionality to control the spiking characteristics paves a pathway for the development of touch-perception sensors that can convert local pressure into electrical spikes for further processing in neural networks. We have proposed a mathematical model which captures the operation principle of the fabricated memristive sensors and qualitatively describes the measured spiking behaviour. Employing such flexible diffusive memristors that can directly translate tactile information into spikes, similar to force and pressure sensors, could offer substantial benefits for various applications in robotics.

Piezoelectric Actuator-Driven Pulsed Water Jet for Neurosurgery: Laboratory Evaluation with the Swine Model and Implications of Mechanical Properties.

Pulsed water jet is an emerging surgical instrumentation intended to achieve both maximal lesion resection and functional maintenance through preservation of fine vessels and minimal damage to the surrounding tissue. The piezoelectric actuator-driven pulsed water jet (ADPJ) is a new technology that can deliver a precisely controlled uniform and efficient pulsed water jet with minimum water flow. The present study evaluated the ADPJ system in preclinical animal studies in the swine brain, and investigated breaking strength, one of the parameters for mechanical properties, to elucidate the mechanism of tissue selectivity for tissue dissection by the water jet. This system consisted of a pump chamber driven by a piezoelectric actuator, a stainless steel tube, and a nozzle (internal diameter: 0.15 mm). Water was supplied at 6 ml/min. The relationship between input voltage (3-25 V at 400 Hz) and peak pressure was measured using a pressure sensor through a sensing hole. The temporal profile of dissection depth during moving application was evaluated using gelatin brain phantom and swine brain. The dissected specimens were evaluated histologically. The mechanical property (breaking strength) of the swine brain was measured by a compact table-top universal tester. Peak pressure increased linearly with increase in input voltage, which reflected the dissection depth in both the gelatin brain phantom and swine brain. Small arteries were preserved, and minimum damage to surrounding tissues occurred. The breaking strength of the arachnoid membrane (0.12 ± 0.014 MPa) was significantly higher compared with the gray matter (0.030 ± 0.010 MPa) and white matter (0.056 ± 0.009 MPa; p < 0.05). The breaking strength of the gray matter corresponded to that of 3 wt% gelatin, and that of white matter corresponded to a value between 3.5 and 4 wt% gelatin, and the dissection depth seemed to be estimated at 3 to 4 wt% gelatin. The present study suggests that the ADPJ system has the potential to achieve accurate tissue dissection with preservation of blood vessels in neurosurgery. The difference in breaking strength may explain the tissue selectivity between the brain parenchyma and tissue protected by the arachnoid membrane.