Flexible Resistive Sensor Research Articles

Design, Calibration and Morphological Characterization of a Flexible Sensor with Adjustable Chemical Sensitivity and Possible Applications to Sports Medicine.

Active life monitoring via chemosensitive sensors could hold promise for enhancing athlete monitoring, training optimization, and performance in athletes. The present work investigates a resistive flex sensor (RFS) in the guise of a chemical sensor. Its carbon 'texture' has shown to be sensitive to CO2, O2, and RH changes; moreover, different bending conditions can modulate its sensitivity and selectivity for these gases and vapors. A three-step feasibility study is presented including: design and fabrication of the electronic read-out and control; calibration of the sensors to CO2, O2 and RH; and a morphological study of the material when interacting with the gas and vapor molecules. The 0.1 mm-1 curvature performs best among the tested configurations. It shows a linear response curve for each gas, the ranges of concentrations are adequate, and the sensitivity is good for all gases. The curvature can be modulated during data acquisition to tailor the sensitivity and selectivity for a specific gas. In particular, good results have been obtained with a curvature of 0.1 mm-1. For O2 in the range of 20-70%, the sensor has a sensitivity of 0.7 mV/%. For CO2 in the range of 4-80%, the sensitivity is 3.7 mV/%, and for RH the sensitivity is 33 mV/%. Additionally, a working principle, based on observation via scanning electron microscopy, has been proposed to explain the chemical sensing potential of this sensor. Bending seems to enlarge the cracks present in the RFS coverage; this change accounts for the altered selectivity depending on the sensor's curvature. Further studies are needed to confirm result's reliability and the correctness of the interpretation.

Tennis action recognition and evaluation with inertial measurement unit and SVM

Action recognition in tennis plays a crucial role for athletes and coaches, aiding in understanding and evaluating the players' skill levels to formulate more effective training plans and tactical strategies. To enhance the recognition and grading of tennis player actions, this study introduces the use of inertial measurement units and flexible resistive sensors for data collection. An improved Support Vector Machine is employed for data classification to achieve efficient action recognition. The results demonstrated that the proposed classification algorithm achieved an average accuracy of 95.35 % in recognizing actions of elite athletes, with the highest accuracy (96.38 %) observed in forehand strokes. In the case of sub-elite athletes, the algorithm achieved an impressive average accuracy of 97.67 %. For amateur enthusiasts, the algorithm exhibited an average accuracy of 94.08 %. Furthermore, elite athletes exhibited larger peak values in the three-axis acceleration waveform during ball striking. Specifically, the absolute peak value of acceleration in the Y-axis for elite athletes reached 78 m/s², representing an increase of 39 m/s² and 8 m/s² compared to the other two levels of athletes, respectively. Additionally, on the X and Z axes, elite athletes' acceleration peak values reached 59 m/s² and 78 m/s², significantly higher than those of sub-elite athletes and amateur enthusiasts. Moreover, the acceleration curves of elite athletes demonstrated a higher overall regularity. These findings indicate that the proposed action recognition method has a significant impact on recognition and evaluation, providing valuable insights for action recognition and assessment across various domains and advancing the application of artificial intelligence technology in the field of sports.

Characterization of fatigue behavior of 3D printed pneumatic fluidic elastomer actuators

Soft robots have gained significant interest due to their high flexibility and adaptability to various working conditions. Recent advancements in engineering and innovative materials have enabled the design and production of sophisticated soft robotic systems with enhanced capabilities. This study aims to evaluate the fatigue behavior of bellow-type pneumatic soft actuators fabricated through fused filament fabrication (FFF) using thermoplastic polyurethane (TPU), compared to silicone rubber cast actuators. The actuators were equipped with resistive flex sensors to monitor bending motion, and fatigue tests were performed with cycles of inflation and deflation until failure. Results showed that 3D printed TPU actuators could withstand a significant number of cycles before failure, with an average of 6410 cycles at 3 bar pressure, compared to 3439 cycles at 1 bar pressure for the silicone actuators. The study identified a set of fabrication parameters that positively affect the durability of TPU actuators, providing valuable insights for replicating these results. Additionally, the study established a plausible range of utilization for 3D-printed FFF actuators in terms of the number of cycles they can endure, offering critical data for engineers and designers to make informed decisions about the design and application of these actuators in various practical scenarios. The findings demonstrate the potential of FFF for producing durable, long-lasting pneumatic soft actuators.

Ultra-sensitive flexible resistive sensor based on modified PEDOT: PSS inspired by earthworm

Flexible resistive sensors currently face challenges such as low sensitivity and poor stability. In this work, inspired by the skin structure of earthworm, a double strain layer with annular cracks aiming to achieve precise construction of conductive pathways in strain materials is proposed. This design strategy enhances the sensitivity, detection range, stability, and consistency of flexible resistive sensor. The sensing materials utilized primarily consist of mechanically and electrically modified PEDOT:PSS. The sensor showcases high sensitivity (with a gauge factor up to 1973.83), a wide detection range (0 ∼ 10 %), rapid response time (60–70 ms), and exceptional repeatability (1000 cycles). The sensor is integrated into finger clips for measurement of pulse wave. By analyzing features extracted from pulse wave, neural networks are employed for the prediction of encompassing coronary heart disease (CHD), atrial septal defect (ASD), atrial fibrillation (AF) and blood pressure levels. The classification accuracy of CHD, ASD and AF surpasses 96 %. The average prediction errors for systolic blood pressure, diastolic blood pressure and mean arterial pressure are found to be −0.3455, −0.2025 and −0.0319, respectively, meeting the standards set by the Association for the Advancement of Medical Instrumentation and the British Hypertension Society Class A criteria.

Highly Stretchable and Multimodal MXene/CNTs/TPU Flexible Resistive Sensor with Hierarchical Structure Inspired by Annual Ring for Hand Rehabilitation.

With the advent of the intelligent age and people's higher pursuit of health, wearable sensors with functions of health monitoring and assisting physical rehabilitation are increasingly favored by consumers. Wherein, highly stretchable flexible sensors show promising potential, but the unstable conductivity under large strains remains a great challenge to develop flexible wearable sensors with both a wide work range and strain insensitivity. Based on this, a MXene/CNTs/TPU flexible resistive sensor (MCT/FRS) with hierarchical structure inspired by the annual ring was proposed. Benefiting from the bioinspired structure with tightly warped inner layers and deformable spring structure outside, the MCT/FRSs enable stable sensing over a wide working range of up to 700% under the stretching mode, as well as superior durability (7500 cycles). It also possessed linear and adjustable piezoresistive properties under the compression mode. Finally, the sensor was not only successfully employed for monitoring various human movements but also was utilized to assist hand rehabilitation in patients with Guillain-Barré syndrome in both stretching and compression modes. This work provides promising and attractive solutions for flexible wearable devices and intelligent medical care.

Fabrication of Textured Ni-Coated Carbon Tubes for a Flexible Strain Sensor: Effect of the Device Elastic Modulus on Sensor Performance.

The exploration of flexible resistive sensors with excellent performance remains a challenge. In this paper, a nickel-coated carbon tube with a textured structure was prepared as a conductive sensitive material and inserted into the poly(dimethylsiloxane) (PDMS) polymer; interestingly, the sensor performance was controlled by the elastic modulus of the matrix resin. The results show that Pd2+ may be adsorbed by the active groups on the surface of a plant fiber as a catalytic center for the reduction of Ni2+. After 300 °C annealing, the inner plant fiber would be carbonized and attached to the outside of the nickel tube; to be precise, the textured Ni-encapsulated C tube was fabricated successfully. It is worth noting that the C tube serves as a layer of support for the external Ni coating, providing sufficient mechanical strength. In addition, resistance sensors with different properties were prepared by controlling the elasticity modulus of the PDMS polymer by introducing different contents of curing agents. The limit uniaxial tensile strain was enhanced from 42 to 49% and sensitivity reduced from 0.2 to 2.0% with the elasticity modulus of the matrix resin increasing from 0.32 to 2.2 MPa. As expected, the sensor is obviously appropriate for the detection of elbow joints, human speaking, and human joints with the reduction of the elasticity modulus of the matrix resin. To be precise, the optimal elastic modulus of the sensor matrix resin would facilitate the improvement of its sensitivity to monitor different human behaviors.

PSS modified by 3-aminopropyltrimethoxysilane linking large-area GNPs/PSS to silicone rubber with stable interface combination for high sensitivity flexible resistive sensor

Flexible resistive sensors based on crack structures have been widely studied in the field of electronic skin and health monitoring. However, it is still a challenge to develop resistive sensors with strong interface bonding forces due to the problem that the sensitive layer of traditional cracked sensors falls off easily. This paper reports a new type of graphene-based resistive sensor, which uses modified poly(sodium 4-phenylene sulfonate) and graphene nanosheets to prepare a highly conductive, fragile, film sensitive layer. Siloxane at the end of modified poly(sodium 4-phenylene sulfonate) is used to enhance the interface between the sensitive layer and the silicone rubber layer to prepare a flexible resistive sensor with a crack structure. The sensor has a high sensitivity (gauge factor up to 21980.28) and a high response speed (∼65 ms), with an effective working range of 0–4.7 % strain. The sensor has been successfully applied to human physiological signal detection. The neural network algorithm successfully predicted the corresponding blood pressure based on the pulse wave signal detected by the sensor. The average error of systolic blood pressure and diastolic blood pressure was 0.79 and 0.42 mmHg, and the error of ± 5 mmHg accounted for 91.2 % and 94.3 % of the total, respectively. The error value passed the Association for the Advancement of Medical Instrumentation standard, and reached the Class A standard of British Hypertension Society.

An Ultra‐Sensitive Flexible Resistive Sensor with Double Strain Layer and Crack Inspired by the Physical Structure of Human Epidermis: Design, Fabrication, and Cuffless Blood Pressure Monitoring Application

AbstractThe cuffless blood pressure (BP) monitoring device has attracted much attention because of its comfortable and real‐time monitoring. Inspired by the physical structure of the human epidermis, an ultra‐sensitive flexible resistive sensor with a double strain layer and crack structure is prepared by screen printing in this study. The strain material of the sensor is mainly prepared by blending Poly(3,4‐ethylenedioxythiophene) :poly(styrenesulfonate) (PEDOT:PSS) with waterborne polyurethane and reduced graphene oxide. The sensor exhibits a high gauge factor (GF, ≈1682), fast response (≈48 ms), and long‐term stability (> 2000 cycles) at low strain (≈0–5%). The sensor is placed on the human radial artery and can accurately measure the pulse wave. The features of the pulse wave are automatically extracted using a convolutional neural network. Then the features are corrected using a long short‐term memory neural network, and the current BP value is predicted using a fully connected network layer. The BP result meets the A‐level standards of the Association for the Advancement of Medical Instrumentation and the British Hypertension Society. This study provides an efficient device and method for continuous and non‐invasive BP monitoring.

Dynamic Measurement Assessments of Sensory Gloves Based on Resistive Flex Sensors and Inertial Measurement Units

Sensory gloves are devices capable of measuring finger movements and are useful in numerous applications, many of which require real-time data acquisition. However, the procedures explored in the literature to assess measurement repeatability and reliability mainly rely on static or quasi-static conditions. To overcome this limitation, here we propose a testing procedure for assessing measurements under dynamic conditions (slow, medium, and rapid finger joint movements). To this aim, we used two sensory gloves, one based on resistive flex sensors (RFSs) and another based on inertial measurement units (IMUs) – as two of the most adopted types. Our study demonstrated the feasibility of measuring dynamic finger movements and the differences in dynamic measurement repeatability and reliability between RFS- and IMU-based gloves when considering the angles (in degrees) of each finger joint. The RFS-based glove scored with an average range ± standard deviation (SD) of 6.84 ± 2.77°, and an intraclass correlation coefficient (ICC) of 0.77 ± 0.14, whereas the IMU-based glove scored with an average range of 8.49 ± 2.72°, and an ICC of 0.75 ± 0.14. Both gloves exhibited better repeatability and reliability at the slowest speed, with the RFS-based glove having an higher repeatability than the IMU-based one ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> < 0.001). Moreover, when compared to previous studies, the results (in terms of reliability and repeatability) here obtained under dynamic conditions are comparable to those obtained under static or quasi-static conditions. In summary, our results indicate that both proposed sensory gloves are suitable for most applications that require dynamic interactions.

An ultra-sensitive core-sheath fiber strain sensor based on double strain layered structure with cracks and modified MWCNTs/silicone rubber for wearable medical electronics

Flexible resistive sensors have attracted widespread attention in the field of wearable electronics. Inspired by the single-layer crack structure which has ultra-high sensitivity, a double strain layered crack structure is proposed to expand the maximum working strain (30%) of flexible sensor while maintaining the ultra-high sensitivity (GF = 2165 at 0–30% and 7381 at 25–30%). MWCNTs modified by 3-Aminopropyltriethoxysilane were filled into silicon rubber to form nanocomposites with fast response times (42 ms), high repeatability (>1000) and low hysteresis (7.08%). Furthermore, the respiratory rate, heart rate and blood pressure can be measured by the proposed sensor. The average errors of the respiratory rate and heart rate are less than 2% and 3% respectively. The mean error of systolic blood pressure, diastolic blood pressure and mean blood pressure is −0.0042 mmHg, 0.3730 mmHg, and 0.2472 mmHg respectively, meeting the standards of Association for the Advancement of Medical Instrumentation and A-level British Hypertension Society. This study shows that the proposed sensor has broad application prospects in high-precision wearable medical electronics.

Nonlinear Estimation and Control of Bending Soft Pneumatic Actuators Using Feedback Linearization and UKF

In this article, we combine nonlinear estimation and control methods for precise bending angle control in soft pneumatic actuators driven by a pressure source and single low-cost <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> / <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> solenoid valve. First, a complete model for the soft actuator is derived, which includes both the motion and pressure dynamics. An unscented Kalman filter (UKF) is used to estimate the velocity state and filter noisy measurements from a pressure sensor and an embedded resistive flex sensor. Then, a feedback linearization approach is used with pole placement and linear quadratic regulator (LQR) controllers for bending angle control. To compensate for model uncertainties and improve reference tracking, integral action is incorporated to both controllers. The closed-loop performance of the nonlinear estimation and control approach is experimentally evaluated with a soft pneumatic network actuator. The simulation and experimental results show that the UKF provides accurate state estimation from noisy sensor measurements. The results demonstrate the effectiveness and robustness of the proposed observer-based nonlinear controllers for bending angle trajectory tracking.

A Single-material-printed, Low-cost design for a Carbon-based fabric strain sensor

The manufacturing of flexible strain sensors for wearable electronics usually requires different conductive materials for the sensing part and the connection part. This increases the complexity, cost, and performance issues due to the mismatch of the thermo-electro-mechanical properties of the materials. Herein, a new design scheme using a single conductive material is presented for a low-cost mass-producible fabric strain sensor, where a carbon/silicone nanocomposite is screen-printed to make both parts. By exploring the dimension effect and modelling of the conductive tracks, and adopting a large difference of over 100 times in aspect ratio, this research makes the electrical response of the fabric strain sensor depend almost exclusively on the sensing part, while its connection part has a low resistance. The sensor exhibits outstanding performance with a wide working range (60% strain), adequate linearity, long fatigue life (∼50,000 cycles), and mechanical robustness, rendering it suitable for human body movement detection. Moreover, the manufacturing process is simple and low-cost ($11 per m2). Thus, the new design scheme overcomes the mismatch issue and provides an important reference value for the design of flexible resistive sensors working in a high resistance range, from ∼ 100 KΩ to several MΩ.

Highly stretchable and sensitive flexible resistive strain sensor based on waterborne polyurethane polymer for wearable electronics

In recent years, flexible resistive sensors have attracted wide attention as one of the important components of wearable devices. However, it is difficult for most flexible sensors to have high stretchability and high sensitivity at the same time. In this study, the modified multi-walled carbon nanotubes as conductive fillers were filled into waterborne polyurethane to form conductive composites, leading to high stretchability of the sensor (120%). The regular crack structure was designed into the sensor to achieve a high gauge factor (∼2000), and the consistency of the sensor was realized by combining the screen-printing process. In addition, the flexible sensor has a short response time (90 ms), good long-term stability (>1000 cycles), and hysteresis characteristics. Furthermore, the excellent characteristics of the flexible sensor enabled it to have a wide working range for human motion and physiological detection, and the sensor was also successfully applied to Morse Code communication and human motion posture recognition for elder fall alarm. This provides a solution for applications in the field of medical health and human-computer interaction.

Artificial intelligence (AI)-driven smart glove for object recognition application

The development of soft sensors with high sensitivities and good response time is particularly useful for healthcare and robotics applications. This article presents a smart glove with an object recognition feature using the support vector machine (SVM), a supervised machine learning algorithm. A smart glove was fabricated using five resistive flex sensors, which can be attached on each of the fingers, respectively. The flex sensor was characterized. A microcontroller was used to receive and process the data from the flex sensor and transmit it to a PC for machine learning and prediction. The SVM model was trained using 160 training data set, and the trained model could be used to recognize three objects with different shapes with an accuracy of 91.88%. The proposed AI-based smart glove has shown high accuracy in object recognition. The proposed approach can possibly provide a promising low-cost solution for the healthcare and robotics industries.

A Tension/Pressure Integrated Resistive Sensor Comprising of a PDMS-LC-MWCNT Composite.

A flexible strain sensor which integrates both pressure sensing and tension sensing functions is demonstrated with an active layer comprising of polydimethy-lsiloxane (PDMS) elastomer, liquid crystal (LC), and multi-walled carbon nanotubes (MWCNTs). The introduction of LC improves the agglomeration of MWCNTs in PDMS and decreases Young’s modulus of flexible resistive sensors. The tension/pressure integrated resistive sensor not only shows a broad tensile sensing range of 140% strain but also shows a good sensitivity of the gauge factor, 40, with tensile force. Besides, the tension/pressure integrated resistive sensor also shows good linearity and sensitivity under pressure. The resistance of the pressure sensor increases as the applied pressure increases because of the decrease in the cross-sectional area of the path. The sensor also shows good hydrophobic properties which may help it to work under complex environment. The tension/pressure integrated sensor shows great promising applications in electronic skins and wearable devices.

16.4: A multifunctional flexible resistive sensor with a Carbon Nanotube‐Liquid Crystal‐PDMS Composite

A multifunctional flexible resistive sensor which integrates both pressure sensing and tension sensing functions is demonstrated with an active layer comprising of liquid crystal(LC), multi‐walled carbon nanotube (MWCNT), and polydimet hy‐lsiloxane(PDMS) elastomer. The introduction of LC improves the agglomeration of MWCNTs in PDMS and improves the performance of the sensor. The sensor shows a wide tensile sensing range of 140% strain and a good sensitivity with a gauge factor of 40 with tensile force, and shows good linearity and sensitivity under pressure. The sensor also shows good hydrophobic property which may help it to work under complex environment. The multifunctional sensor offers great potential applications in emerging wearable devices and electronic skins.

Human Signature Identification Using IoT Technology and Gait Recognition

This study aimed to develop an autonomous design system for recognizing the subject by gait posture. Gait posture is a type of non-verbal communication characteristic of each person, and can be considered a signature used in identification. This system can be used for diagnosis. The system helps aging or disabled subjects to identify incorrect posture to recover the gait. Gait posture gives information for subject identification using leg movements and step distance as characteristic parameters. In the current study, the inertial measurement units (IMUs) located in a mobile phone were used to provide information about the movement of the upper and lower leg parts. A resistive flex sensor (RFS) was used to obtain information about the foot contact with the ground. The data were collected from a target group comprising subjects of different age, height, and mass. A comparative study was undertaken to identify the subject after the gait posture. Statistical analysis and a machine learning algorithm were used for data processing. The errors obtained after training data are presented at the end of the paper and the obtained results are encouraging. This article proposes a method of acquiring data available to anyone by using indispensable devices purchased by all users such as mobile phones.

Sm2O3 rice-like nanorods decorated on rGO flexible resistive sensor for room temperature LPG detection

Flexible electrochemical sensors have gained huge attention over their significant properties and the probability of applying to any wearable electronic devices. LPG traces in definite concentrations leads to a fire accident, adverse health effects, and even causes death. The current study focuses on a flexible, room temperature LPG sensor based on Sm2O3/rGO hybrid nanocomposite with different loading factors (1, 3, and 5 wt%) coated on flexible polyethylene terephthalate (PET) substrate working electrodes. Eggshell membrane (ESM) template constituents are acts as a reducing agent during the hydrothermal synthesis of Sm2O3 nanorods. Flexible Sm2O3/rGO hybrid Chemiresistive gas sensor was tested for various gas sensing parameters. The decoration of Sm2O3 nanorods on 3 wt% rGO sheets exhibited good selectivity to liquefied petroleum gas (LPG) at room temperature gives a response of 116% at 700 ppm of LPG with a short response, recovery time and gained 99% response after bending with 94.5% stability of the sensor.

Efficient Multiaxial Shoulder-Motion Tracking Based on Flexible Resistive Sensors Applied to Exosuits.

This article describes the performance of a flexible resistive sensor network to track shoulder motion. This system monitors every gesture of the human shoulder in its range of motion except rotations around the longitudinal axis of the arm. In this regard, the design considers the movement of the glenohumeral, acromioclavicular, sternoclavicular, and scapulothoracic joints. The solution presented in this work considers several sensor configurations and compares its performance with a set of inertial measurement units (IMUs). These devices have been put together in a shoulder suit with Optitrack visual markers in order to be used as pose ground truth. Optimal configurations of flexible resistive sensors, in terms of accuracy requirements and number of sensors, have been obtained by applying principal component analysis techniques. The data provided by each configuration are then mapped onto the shoulder pose by using neural network algorithms. According to the results shown in this article, a set of flexible resistive sensors can be an adequate alternative to IMUs for multiaxial shoulder pose tracking in open spaces. Furthermore, the system presented can be easily embedded in fabric or wearable devices without obstructing the user's motion.

Humidity and Strain Rate Determine the Extent of Phase Shift in the Piezoresistive Response of PEDOT:PSS.

The piezoresistive response of PEDOT:PSS is sensitive to changes in its morphology when exposed to humidity and in response to different strain rates. The piezoresistive response of as-cast PEDOT:PSS transitions from being in-phase to being out-of-phase with applied strain when the relative humidity is reduced from >50% to near zero. At >50% relative humidity, the PSS matrix swells and interrupts the connectivity of electrically conducting PEDOT domains. Stretching PEDOT:PSS at such conditions leads to an increase in resistance with strain. Under dry conditions, PEDOT domains are connected; stretching PEDOT:PSS instead leads to preferential alignment of the conducting domains and a concomitant decrease in resistance. At intermediate humidity, the piezoresistive response of PEDOT:PSS is phase shifted relative to applied strain, with it being out-of-phase at low strain rates (0.34%/min) and in-phase at high strain rates (1.12%/min). We interpret this peculiar and surprising observation as a competition between strain-induced domain separation and alignment, each having a different response time to applied strain. Postdeposition treatment of PEDOT:PSS with dichloroacetic acid removes excess PSS; PEDOT:PSS's piezoresistive response is then invariant with humidity and strain rate. Stabilizing its piezoresistive response can ensure accuracy of PEDOT:PSS-based flexible resistive sensors whose response to small strains is used to monitor environmental and human-health.