Fabrication of a Capacitive 3D Spacer Fabric Pressure Sensor with a Dielectric Constant Change for High Sensitivity.

Smart wearable sensors are increasingly integrated into everyday life, interfacing with the human body to enable real-time monitoring of biological signals. This study focuses on creating high-sensitivity capacitive-type sensors by impregnating polyester-based 3D spacer fabric with a Carbon Nanotube (CNT) dispersion. The unique properties of conductive particles lead to nonlinear variations in the dielectric constant when pressure is applied, consequently affecting the gauge factor. The results reveal that while the fabric without CNT particles had a gauge factor of 1.967, the inclusion of 0.04 wt% CNT increased it significantly to 5.210. As sensor sensitivity requirements vary according to the application, identifying the necessary CNT wt% is crucial. Artificial intelligence, particularly the Multilayer Perception (MLP) model, enables nonlinear regression analysis for this purpose. The MLP model created and validated in this research showed a high correlation coefficient of 0.99564 between the model predictions and actual target values, indicating its effectiveness and reliability.

Development and Evaluation of Vibration Canceling System Utilizing Macro-Fiber Composites (MFCs) and Long Short-Term Memory (LSTM) Vibration Prediction AI Algorithms for Road Driving Vibrations.

This study developed an innovative active vibration canceling (AVC) system designed to mitigate non-periodic vibrations during road driving to enhance passenger comfort. The macro-fiber composite (MFC) used in the system is a smart material that is flexible, soft, lightweight, and applicable in many fields as a dual-purpose sensor and actuator. The target vibrations are road vibration data that were collected while driving on standard urban (Seoul) and highway roads at 40 km/s. To predict and cancel the target vibration accurately before passing it, we modeled the vibration prediction algorithm using a long short-term memory recurrent neural network (LSTM RNN). We regenerated vibrations on Seoul and highway roads at 40 km/s using MFCs and measured the displacements of the measured, predicted, and AVC vibrations of each road condition. To evaluate the vibration, we computed the root mean squared error (RMSE) and compared standard deviation (SD) values. The accuracies of LSTM RNN vibration prediction algorithms are 97.27% and 96.36% on Seoul roads and highway roads, respectively, at 40 km/s. Although the vibration ratio compared with the AVC results are different, there was no difference between the values of the AVC vibrations. According to a previous study and the principle of the AVC system, the target vibrations decrease by canceling the inverse vibration of the MFC actuator.

Measurement of 3D Wrist Angles by Combining Textile Stretch Sensors and AI Algorithm.

The wrist is one of the most complex joints in our body, composed of eight bones. Therefore, measuring the angles of this intricate wrist movement can prove valuable in various fields such as sports analysis and rehabilitation. Textile stretch sensors can be easily produced by immersing an E-band in a SWCNT solution. The lightweight, cost-effective, and reproducible nature of textile stretch sensors makes them well suited for practical applications in clothing. In this paper, wrist angles were measured by attaching textile stretch sensors to an arm sleeve. Three sensors were utilized to measure all three axes of the wrist. Additionally, sensor precision was heightened through the utilization of the Multi-Layer Perceptron (MLP) technique, a subtype of deep learning. Rather than fixing the measurement values of each sensor to specific axes, we created an algorithm utilizing the coupling between sensors, allowing the measurement of wrist angles in three dimensions. Using this algorithm, the error angle of wrist angles measured with textile stretch sensors could be measured at less than 4.5°. This demonstrated higher accuracy compared to other soft sensors available for measuring wrist angles.

A Wearable Strain Sensor Utilizing Shape Memory Polymer/Carbon Nanotube Composites Measuring Respiration Movements.

Flexible wearable sensors are integral in diverse applications, particularly in healthcare and human-computer interaction systems. This paper introduces a resistive stretch sensor crafted from shape memory polymers (SMP) blended with carbon nanotubes (CNTs) and coated with silver paste. Initially, the sensor's characteristics underwent evaluation using a Universal Testing Machine (UTM) and an LCR meter. These sensors showcased exceptional sensitivity, boasting a gauge factor of up to 20 at 5% strain, making them adept at detecting subtle movements or stimuli. Subsequently, the study conducted a comparison between SMP-CNT conductors with and without the silver coating layer. The durability of the sensors was validated through 1000 cycles of stretching at 4% ∆R/R0. Lastly, the sensors were utilized for monitoring respiration and measuring human breathing. Fourier transform and power spectrum density (PSD) analysis were employed to discern frequency components. Positioned between the chest and abdominal wall for contact-based respiration monitoring, the sensors revealed a dominant frequency of approximately 0.35 Hz. Signal filtering further enhanced their ability to capture respiration signals, establishing them as valuable tools for next-generation personalized healthcare applications.

Comparative Performance Analysis of Inverse Phase Active Vibration Cancellation Using Macro Fiber Composite (MFC) and Vibration Absorption of Silicone Gel for Vibration Reduction.

This study focuses on addressing the issue of unwanted vibrations commonly encountered in various fields by designing an Active Vibration Cancellation (AVC) structure using a flexible piezoelectric composite material macro fiber composite (MFC). A comparative performance analysis was conducted between the AVC and a traditional passive gel that continuously absorbs vibrations. The results showed that AVC was more effective in mitigating vibrations, making it a promising solution for vibration control. The results of this study from extensive vibration-sensing experiments and comparisons revealed that AVC effectively cancels the vibrations and vibration absorption performance of the passive gel. These findings underline the potential of AVC as an efficient method for eliminating and managing undesired vibrations in practical applications. Specifically, AVC demonstrated a high vibration cancellation ratio of approximately 0.96 at frequencies above 10 Hz. In contrast, passive gel exhibited a relatively consistent vibration absorption ratio, approximately 0.70 to 0.75 at all tested frequencies. These quantitative findings emphasize the superior performance of AVC in reducing vibrations to levels below a certain threshold, demonstrating its efficacy for vibration control in real-world scenarios.

Prediction of Electrical Resistance with Conductive Sewing Patterns by Combining Artificial Neural Networks and Multiple Linear Regressions.

This study aims to estimate the impact of sewing thread patterns on changes in the resistance of conductive yarns coated with silver paste. Firstly, the structure of the conductive yarns was examined, and various variations in the length and angle of individual sewing stitches were observed and analyzed through experiments. The results revealed that as the length of an individual stitch decreased, the width of the conductive yarn increased. Additionally, variations in the stitch angle resulted in different resistance values in the conductive yarn. These findings provide essential information for optimizing sewing patterns and designing components. Secondly, the comparison between models using multiple linear regression analysis and sewing neural networks was included to show optimized resistance prediction. The multiple linear regression analysis indicated that the stitch length and angle were significant variables affecting the resistance of the conductive thread. The artificial neural network model results can be valuable for optimizing sewing patterns and controlling resistance in various applications that utilize conductive thread. In addition, understanding the resistance variation in conductive thread according to sewing patterns and using optimized models to enhance component performance provides opportunities for innovation and progress. This research is necessary for the textile industry and materials engineering fields and holds high potential for practical applications in industrial settings.

Highly Inductive Coil Spring Strain/Compress Sensors Integrated with Shape Memory Alloy and Shape Memory Polymers-CNTs.

Future wearable electronic gadgets offer great potential for using stretchable, strain-sensitive materials to instantly detect human motion and record physiological information. This paper presens a strain/compress sensor made from a Shape memory alloy (SMA) coil spring covered with silver pastes and the composite of carbon nanotubes and Shape memory polymer (SMP). The combination of the shape memory materials that expand or contract automatically by temperature improved the mechanics of the sensor. First, the proposed sensors showed an excellent ability to broad-range strain of 250% and compress of 50% with a relative inductance (∆L/L0 ) range from -35% to 50%, respectively. Durability during 1000 loading and unloading cycles at 200% strain is included. Secondly, by monitoring changes in resistance, inductance, and time, it is determined how many silver layers appropriate for transformation should be in order to improve the recovery time of the SMA coil spring. Moreover, the presence of CNTs in the composite-covered outer of sensors helps to reduce the influence of the relation between resistance and temperature in the range from 30 °C to 110 °C. Finally, a device is suggested for monitoring arm and triceps brachii muscle movements based on the stretchable area as a key parameter.

Health Monitoring System from Pyralux Copper-Clad Laminate Film and Random Forest Algorithm.

Sensor technologies have been core features for various wearable electronic products for decades. Their functions are expected to continue to play an essential role in future generations of wearable products. For example, trends in industrial, military, and security applications include smartwatches used for monitoring medical indicators, hearing devices with integrated sensor options, and electronic skins. However, many studies have focused on a specific area of the system, such as manufacturing processes, data analysis, or actual testing. This has led to challenges regarding the reliability, accuracy, or connectivity of components in the same wearable system. There is an urgent need for studies that consider the whole system to maximize the efficiency of soft sensors. This study proposes a method to fabricate a resistive pressure sensor with high sensitivity, resilience, and good strain tolerance for recognizing human motion or body signals. Herein, the sensor electrodes are shaped on a thin Pyralux film. A layer of microfiber polyesters, coated with carbon nanotubes, is used as the bearing and pressure sensing layer. Our sensor shows superior capabilities in respiratory monitoring. More specifically, the sensor can work in high-humidity environments, even when immersed in water-this is always a big challenge for conventional sensors. In addition, the embedded random forest model, built for the application to recognize restoration signals with high accuracy (up to 92%), helps to provide a better overview when placing flexible sensors in a practical system.

Fabrication and Characterization of Three-Dimensional Microelectromechanical System Coaxial Socket Device for Semiconductor Package Testing.

With the continuous reduction in size and increase in density of semiconductor devices, there is a growing demand for contact solutions that enable high-speed testing in automotive, 5G, and artificial intelligence-based devices. Although existing solutions, such as spring pins and rubber sockets, have been effective in various applications, there is still a need for new solutions that accommodate fine-pitch, high-speed, and high-density requirements. This study proposes a novel three-dimensional microelectromechanical system spring structure coaxial socket for semiconductor chip package testing. The socket design incorporates impedance matching for high-speed testing and addresses the challenges of fine-pitch and high-density applications. Mechanical tests are conducted to evaluate the durability of the structure and electrical tests are performed to verify electrical characteristics by utilizing a vector network analyzer up to 60 GHz. Our results have revealed promising performance and will help in further optimizing the design for potential production in the field and industry.

Classification of Breathing Signals According to Human Motions by Combining 1D Convolutional Neural Network and Embroidered Textile Sensor.

Research on healthcare and body monitoring has increased in recent years, with respiratory data being one of the most important factors. Respiratory measurements can help prevent diseases and recognize movements. Therefore, in this study, we measured respiratory data using a capacitance-based sensor garment with conductive electrodes. To determine the most stable measurement frequency, we conducted experiments using a porous Eco-flex and selected 45 kHz as the most stable frequency. Next, we trained a 1D convolutional neural network (CNN) model, which is a type of deep learning model, to classify the respiratory data according to four movements (standing, walking, fast walking, and running) using one input. The final test accuracy for classification was >95%. Therefore, the sensor garment developed in this study can measure respiratory data for four movements and classify them using deep learning, making it a versatile wearable in the form of a textile. We expect that this method will advance in various healthcare fields.

Identification and Classification of Human Body Exercises on Smart Textile Bands by Combining Decision Tree and Convolutional Neural Networks.

In recent years, human activity recognition (HAR) has gained significant interest from researchers in the sports and fitness industries. In this study, the authors have proposed a cascaded method including two classifying stages to classify fitness exercises, utilizing a decision tree as the first stage and a one-dimension convolutional neural network as the second stage. The data acquisition was carried out by five participants performing exercises while wearing an inertial measurement unit sensor attached to a wristband on their wrists. However, only data acquired along the z-axis of the IMU accelerator was used as input to train and test the proposed model, to simplify the model and optimize the training time while still achieving good performance. To examine the efficiency of the proposed method, the authors compared the performance of the cascaded model and the conventional 1D-CNN model. The obtained results showed an overall improvement in the accuracy of exercise classification by the proposed model, which was approximately 92%, compared to 82.4% for the 1D-CNN model. In addition, the authors suggested and evaluated two methods to optimize the clustering outcome of the first stage in the cascaded model. This research demonstrates that the proposed model, with advantages in terms of training time and computational cost, is able to classify fitness workouts with high performance. Therefore, with further development, it can be applied in various real-time HAR applications.

Human Arm Workout Classification by Arm Sleeve Device Based on Machine Learning Algorithms.

Wearables have been applied in the field of fitness in recent years to monitor human muscles by recording electromyographic (EMG) signals. Understanding muscle activation during exercise routines allows strength athletes to achieve the best results. Hydrogels, which are widely used as wet electrodes in the fitness field, are not an option for wearable devices due to their characteristics of being disposable and skin-adhesion. Therefore, a lot of research has been conducted on the development of dry electrodes that can replace hydrogels. In this study, to make it wearable, neoprene was impregnated with high-purity SWCNTs to develop a dry electrode with less noise than hydrogel. Due to the impact of COVID-19, the demand for workouts to improve muscle strength, such as home gyms and personal trainers (PT), has increased. Although there are many studies related to aerobic exercise, there is a lack of wearable devices that can assist in improving muscle strength. This pilot study proposed the development of a wearable device in the form of an arm sleeve that can monitor muscle activity by recording EMG signals of the arm using nine textile-based sensors. In addition, some machine learning models were used to classify three arm target movements such as wrist curl, biceps curl, and dumbbell kickback from the EMG signals recorded by fiber-based sensors. The results obtained show that the EMG signal recorded by the proposed electrode contains less noise compared to that collected by the wet electrode. This was also evidenced by the high accuracy of the classification model used to classify the three arms workouts. This work classification device is an essential step towards wearable devices that can replace next-generation PT.

Development of Embroidery-Type Sensor Capable of Detecting Respiration Using the Capacitive Method.

This study presents a respiration sensor that is dependent on a parallel capacitor, including connection lines and electrodes embroidered on textiles. First, characterizations of the respiration capacitor using a silver thread, including a combination of porous Eco-flex simulating air in the lungs due to respiration, were evaluated using an LCR meter. Second, the effects of air gaps on the detection of respiration motions according to the change in electrode distance under pressure were presented. The data values were measured from 1 to 300 kHz using an LCR meter and dielectric test fixture. Third, actual breathing was examined in four patterns: normal breathing, deep breathing, hyperventilation, and apnea. The test was performed after fabricating a clothing-type breathing sensor. Finally, the change in capacitance for actual respiration was determined by wearing a clothing-type respiration sensor based on the data collected. The effectiveness of the respiration sensor was demonstrated by measuring it to discern all waveforms, cycles, and ranges associated with the breathing pattern.

Sex hormone influence on female-biased autoimmune diseases hints at puberty as an important factor in pathogenesis.

The majority of autoimmune diseases affect more women than men, suggesting an important role for sex hormones in regulating immune response. Current research supports this idea, highlighting the importance of sex hormones in both immune and metabolic regulation. Puberty is characterized by drastic changes in sex hormone levels and metabolism. These pubertal changes may be what forms the gulf between men and women in sex bias towards autoimmunity. In this review, a current perspective on pubertal immunometabolic changes and their impact on the pathogenesis of a select group of autoimmune diseases is presented. SLE, RA, JIA, SS, and ATD were focused on in this review for their notable sex bias and prevalence. Due to both the scarcity of pubertal autoimmune data and the differences in mechanism or age-of-onset in juvenile analogues often beginning prior to pubertal changes, data on the connection between the specific adult autoimmune diseases and puberty often relies on sex hormone influence in pathogenesis and established sex differences in immunity that begin during puberty.

Development of Embroidery-Type Pressure Sensor Dependent on Interdigitated Capacitive Method.

Many studies have been conducted to develop electronic skin (e-skin) and flexible wearable textiles which transform into actual "skin", using different approaches. Moreover, many reports have investigated self-healing materials, multifunctional sensors, etc. This study presents a systematic approach to embroidery pressure sensors dependent on interdigitated capacitors (IDCs), for applications surrounding intelligent wearable devices, robots, and e-skins. The method proposed a broad range of highly sensitive pressure sensors based on porous Ecoflex, carbon nanotubes (CNTs), and interdigitated electrodes. Firstly, characterizations of ICDs embroidering on a cotton fabric using silver conductive thread are evaluated by a precision LCR meter throughout the frequency range from 1 kHz to 300 kHz. The effect of thread density on the performance of embroidered sensors is included. Secondly, the 16451B dielectric test fixture from Keysight is utilized to evaluate the composite samples' dielectric constant accurately. The effect of frequency on sensor performance was evaluated to consider the influence of the dielectric constant as a function of the capacitance change. This study shows that the lower the frequency, the higher the sensitivity, but at the same time, it also leads to instability in the sensor's operation. Thirdly, assessing the volume fraction of CNTs on composites' properties is enclosed. The presence of volume portion CNTs upgrades the bond strength of composites and further develops sensor deformability. Finally, the presented sensor can accomplish excellent performance with an ultra-high sensitivity of 0.24 kPa−1 in low pressure (<25 kPa) as well as a wide detection range from 1 to 1000 kPa, which is appropriate for general tactile pressure rages. In order to achieve high sensor performance, factors such as density, frequency, fabric substrate, and the structure of the dielectric layer need to be carefully evaluated.

Effects of Fe Staple-Fiber Spun-Yarns and Correlation Models on Textile Pressure Sensors.

As an aspect of intelligent clothing, e-textile sensors can flexibly sense and transmit information about human bodies and environments. However, difficulties relating to their technology and the variation in textile materials employed in their manufacture still limit their ability to analyze and be applied. The authors' previous publication deployed a pressure sensor with warp-knitted spacer fabrics, wet-knitted fabrics, Ag-yarns, and Fe-yarns. An equivalent circuit analyzed the resistance behavior with some effects of the Ag-coated twisted yarns. In the present paper, the authors continue to evaluate the correlation model R-ε and the effects of the Fe staple-fiber spun yarns in detail. Together, the two studies provide an extensive understanding of the textile-related elements that affect pressure sensors. In addition, the process and the analysis (correlation model) could bring the textile sensors here developed close to the manufacturing stage, particularly for high precision/adjustable applications. We also develop a simple touch sensor matrix to demonstrate the potential of the sensor and the analyzing method.

The Programmable Design of Large-Area Piezoresistive Textile Sensors Using Manufacturing by Jacquard Processing.

Among wearable e-textiles, conductive textile yarns are of particular interest because they can be used as flexible and wearable sensors without affecting the usual properties and comfort of the textiles. Firstly, this study proposed three types of piezoresistive textile sensors, namely, single-layer, double-layer, and quadruple-layer, to be made by the Jacquard processing method. This method enables the programmable design of the sensor's structure and customizes the sensor's sensitivity to work more efficiently in personalized applications. Secondly, the sensor range and coefficient of determination showed that the sensor is reliable and suitable for many applications. The dimensions of the proposed sensors are 20 × 20 cm, and the thicknesses are under 0.52 mm. The entire area of the sensor is a pressure-sensitive spot. Thirdly, the effect of layer density on the performance of the sensors showed that the single-layer pressure sensor has a thinner thickness and faster response time than the multilayer pressure sensor. Moreover, the sensors have a quick response time (<50 ms) and small hysteresis. Finally, the hysteresis will increase according to the number of conductive layers. Many tests were carried out, which can provide an excellent knowledge database in the context of large-area piezoresistive textile sensors using manufacturing by Jacquard processing. The effects of the percolation of CNTs, thickness, and sheet resistance on the performance of sensors were investigated. The structural and surface morphology of coating samples and SWCNTs were evaluated by using a scanning electron microscope. The structure of the proposed sensor is expected to be an essential step toward realizing wearable signal sensing for next-generation personalized applications.

Waterproof, thin, high-performance pressure sensors-hand drawing for underwater wearable applications.

Wearable sensors, especially pressure sensors, have become an indispensable part of life when reflecting human interactions and surroundings. However, the difficulties in technology and production-cost still limit their applicability in the field of human monitoring and healthcare. Herein, we propose a fabrication method with flexible, waterproof, thin, and high-performance circuits - based on hand-drawing for pressure sensors. The shape of the sensor is drawn on the pyralux film without assistance from any designing software and the wet-tissues coated by CNTs act as a sensing layer. Such sensor showed a sensitivity (~0.2 kPa-1) while ensuring thinness (~0.26 mm) and flexibility for touch detection or breathing monitoring. More especially, our sensor is waterproof for underwater wearable applications, which is a drawback of conventional paper-based sensors. Its outstanding capability is demonstrated in a real application when detecting touch actions to control a phone, while the sensor is dipped underwater. In addition, by leveraging machine learning technology, these touch actions were processed and classified to achieve highly accurate monitoring (up to 94%). The available materials, easy fabrication techniques, and machine learning algorithms are expected to bring significant contributions to the development of hand-drawing sensors in the future.

Carbonized Cotton Fabric-Based Flexible Capacitive Pressure Sensor Using a Porous Dielectric Layer with Tilted Air Gaps.

Flexible and wearable pressure sensors have attracted significant attention owing to their roles in healthcare monitoring and human-machine interfaces. In this study, we introduce a wide-range, highly sensitive, stable, reversible, and biocompatible pressure sensor based on a porous Ecoflex with tilted air-gap-structured and carbonized cotton fabric (CCF) electrodes. The knitted structure of electrodes demonstrated the effectiveness of the proposed sensor in enhancing the pressure-sensing performance in comparison to a woven structure due to the inherent properties of naturally generated space. In addition, the presence of tilted air gaps in the porous elastomer provided high deformability, thereby significantly improving the sensor sensitivity compared to other dielectric structures that have no or vertical air gaps. The combination of knitted CCF electrodes and the porous dielectric with tilted air gaps achieved a sensitivity of 24.5 × 10-3 kPa-1 at 100 kPa, along with a wide detection range (1 MPa). It is also noteworthy that this novel method is low-cost, facile, scalable, and ecofriendly. Finally, the proposed sensor integrated into a smart glove detected human motions of grasping water cups, thus demonstrating its potential applications in wearable electronics.

Flexible wearable sensors - an update in view of touch-sensing.

Nowadays, much of user interface is based on touch and the touch sensors have been common for displays, Internet of things (IoT) projects, or robotics. They can be found in lamps, touch screens of smartphones, or other wide arrays of applications as well. However, the conventional touch sensors, fabricated from rigid materials, are bulky, inflexible, hard, and hard-to-wear devices. The current IoT trend has made these touch sensors increasingly important when it added in the skin or clothing to affect different aspects of human life flexibly and comfortably. The paper provides an overview of the recent developments in this field. We discuss exciting advances in materials, fabrications, enhancements, and applications of flexible wearable sensors under view of touch-sensing. Therein, the review describes the theoretical principles of touch sensors, including resistive, capacitive, and piezoelectric types. Following that, the conventional and novel materials, as well as manufacturing technologies of flexible sensors are considered to. Especially, this review highlights the multidisciplinary approaches such as e-skins, e-textiles, e-healthcare, and e-control of flexible touch sensors. Finally, we summarize the challenges and opportunities that use is key to widespread development and adoption for future research.