Highly Accurate Wearable Piezoresistive Sensors without Tension Disturbance Based on Weaved Conductive Yarn

Wearable blood pressure sensors for cardiovascular monitoring and machine learning algorithms for blood pressure estimation

With advances in materials science and medical technology, wearable sensors have become crucial tools for the early diagnosis and continuous monitoring of numerous cardiovascular diseases, including arrhythmias, hypertension and coronary artery disease. These devices employ various sensing mechanisms, such as mechanoelectric, optoelectronic, ultrasonic and electrophysiological methods, to measure vital biosignals, including pulse rate, blood pressure and changes in heart rhythm. In this Review, we provide a comprehensive overview of the current state of wearable cardiovascular sensors, focusing particularly on those that measure blood pressure. We explore biosignal sensing principles, discuss blood pressure estimation methods (including machine learning algorithms) and summarize the latest advances in cuffless wearable blood pressure sensors. Finally, we highlight the challenges of and offer insights into potential pathways for the practical application of cuffless wearable blood pressure sensors in the medical field from both technical and clinical perspectives.

Stretchable Electronics with Strain-Resistive Performance

Stretchable electronics have attracted tremendous attention amongst academic and industrial communities due to their prospective applications in personal healthcare, human-activity monitoring, artificial skins, wearable displays, human-machine interfaces, etc. Other than mechanical robustness, stable performances under complex strains in these devices that are not for strain sensing are equally important for practical applications. Here, a comprehensive summarization of recent advances in stretchable electronics with strain-resistive performance is presented. First, detailed overviews of intrinsically strain-resistive stretchable materials, including conductors, semiconductors, and insulators, are given. Then, systematic representations of advanced structures, including helical, serpentine, meshy, wrinkled, and kirigami-based structures, for strain-resistive performance are summarized. Next, stretchable arrays and circuits with strain-resistive performance, that integrate multiple functionalities and enable complex behaviors, are introduced. This review presents a detailed overview of recent progress in stretchable electronics with strain-resistive performances and provides a guideline for the future development of stretchable electronics.

A highly sensitive flexible capacitive pressure sensor with hierarchical pyramid micro-structured PDMS-based dielectric layer for health monitoring

Herein, a flexible pressure sensor with high sensitivity was created using a dielectric layer featuring a hierarchical pyramid microstructure, both in simulation and fabrication. The capacitive pressure sensor comprises a hierarchically arranged dielectric layer made of polydimethylsiloxane (PDMS) with pyramid microstructures, positioned between copper electrodes at the top and bottom. The achievement of superior sensing performance is highly contingent upon the thickness of the dielectric layer, as indicated by both empirical findings and finite-element analysis. Specifically, the capacitive pressure sensor, featuring a dielectric layer thickness of 0.5 mm, exhibits a remarkable sensitivity of 0.77 kPa-1 within the pressure range below 1 kPa. It also demonstrates an impressive response time of 55 ms and recovery time of 42 ms, along with a low detection limit of 8 Pa. Furthermore, this sensor showcases exceptional stability and reproducibility with up to 1,000 cycles. Considering its exceptional achievements, the pressure sensor has been effectively utilized for monitoring physiological signals, sign language gestures, and vertical mechanical force exerted on objects. Additionally, a 5 × 5 sensor array was fabricated to accurately and precisely map the shape and position of objects. The pressure sensor with advanced performance shows broad potential in electronic skin applications.

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.

Machine Learning-Enhanced Biomass Pressure Sensor with Embedded Wrinkle Structures Created by Surface Buckling

Flexible piezoresistive sensors are core components of many wearable devices to detect deformation and motion. However, it is still a challenge to conveniently prepare high-precision sensors using natural materials and identify similar short vibration signals. In this study, inspired by microstructures of human skins, biomass flexible piezoresistive sensors were prepared by assembling two wrinkled surfaces of konjac glucomannan and k-carrageenan composite hydrogel. The wrinkle structures were conveniently created by hardness gradient-induced surface buckling and coated with MXene sheets to capture weak pressure signals. The sensor was applied to detect various slight body movements, and a machine learning method was used to enhance the identification of similar and short throat vibration signals. The results showed that the sensor exhibited a high sensitivity of 5.1 kPa-1 under low pressure (50 Pa), a fast response time (104 ms), and high stability over 100 cycles. The XGBoost machine learning model accurately distinguished short voice vibrations similar to those of individual English letters. Moreover, experiments and numerical simulations were carried out to reveal the mechanism of the wrinkle structure preparation and the excellent sensing performance. This biomass sensor preparation and the machine learning method will promote the optimization and application of wearable devices.

Hybrid Pressure Sensor Based on Carbon Nano-Onions and Hierarchical Microstructures with Synergistic Enhancement Mechanism for Multi-Parameter Sleep Monitoring

With the existing pressure sensors, it is difficult to achieve the unification of wide pressure response range and high sensitivity. Furthermore, the preparation of pressure sensors with excellent performance for sleep health monitoring has become a research difficulty. In this paper, based on material and microstructure synergistic enhancement mechanism, a hybrid pressure sensor (HPS) integrating triboelectric pressure sensor (TPS) and piezoelectric pressure sensor (PPS) is proposed. For the TPS, a simple, low-cost, and structurally controllable microstructure preparation method is proposed in order to investigate the effect of carbon nano-onions (CNOs) and hierarchical composite microstructures on the electrical properties of CNOs@Ecoflex. The PPS is used to broaden the pressure response range and reduce the pressure detection limit of HPS. It has been experimentally demonstrated that the HPS has a high sensitivity of 2.46 V/104 Pa (50-600 kPa) and a wide response range of up to 1200 kPa. Moreover, the HPS has a low detection limit (10 kPa), a high stability (over 100,000 cycles), and a fast response time. The sleep monitoring system constructed based on HPS shows remarkable performance in breathing state recognition and sleeping posture supervisory control, which will exhibit enormous potential in areas such as sleep health monitoring and potential disease prediction.

Ag NW-Embedded Coaxial Nanofiber-Coated Yarns with High Stretchability and Sensitivity for Wearable Multi-Sensing Textiles

The emerging intelligent piezoresistive yarn/textile-based sensors are of paramount importance for skin-interface electronics, owing to their unparalleled features including softness, breathability, and easy integration with functional devices. However, employing a facile way to fabricate 1D sensing yarns with mechanical robustness, multi-functional integration, and comfortability is still demanded for satisfying the practical applications. Herein, a facile one-step synchronous conjugated electrospinning and electrospraying technique is innovatively employed to continuously construct an Ag NW-embedded polyurethane (PU) nanofiber sensing yarn (AENSY) with hierarchical architecture. This 1D AENSY with weavability and stretchability can be woven into AENSY textile-based sensors integrated with functions of strain and pressure sensing. In this embedded multi-scale architecture, Ag NWs are evenly embedded and locked in the oriented and twisted PU nanofiber (PUNF) scaffold, forming the hierarchical mechanical sensing layer on the surface of the AENSY with favorable stability. Meanwhile, the presence of the elastic PUNFs enhances porosity, elasticity, and considerable deformation space, which in turn endow the AENSY textile-based sensor with a gauge factor (GF) up to 1010, a pressure sensitivity up to 16.7 N^-1, high stretchability up to 160%, and high stability under long-term cycles. In addition, the AENSY textile-based sensor exhibits light weight and the unique advantage of skin-friendliness with the human body, which can be directly and conformally attached to the curved human skin to monitor the various human movements. Furthermore, the weavable AENSYs can be integrated into smart textiles with sensing arrays, which are capable for spatial pressure and strain mapping. Thus, the continuous one-step developing process and the stable embedded-twisted fiber structure provide a promising strategy to develop innovative smart yarns and textiles for personalized healthcare and human-machine interfaces.

Elastic Fibers/Fabrics for Wearables and Bioelectronics

Wearables and bioelectronics rely on breathable interface devices with bioaffinity, biocompatibility, and smart functionality for interactions between beings and things and the surrounding environment. Elastic fibers/fabrics with mechanical adaptivity to various deformations and complex substrates, are promising to act as fillers, carriers, substrates, dressings, and scaffolds in the construction of biointerfaces for the human body, skins, organs, and plants, realizing functions such as energy exchange, sensing, perception, augmented virtuality, health monitoring, disease diagnosis, and intervention therapy. This review summarizes and highlights the latest breakthroughs of elastic fibers/fabrics for wearables and bioelectronics, aiming to offer insights into elasticity mechanisms, production methods, and electrical components integration strategies with fibers/fabrics, presenting a profile of elastic fibers/fabrics for energy management, sensors, e-skins, thermal management, personal protection, wound healing, biosensing, and drug delivery. The trans-disciplinary application of elastic fibers/fabrics from wearables to biomedicine provides important inspiration for technology transplantation and function integration to adapt different application systems. As a discussion platform, here the main challenges and possible solutions in the field are proposed, hopefully can provide guidance for promoting the development of elastic e-textiles in consideration of the trade-off between mechanical/electrical performance, industrial-scale production, diverse environmental adaptivity, and multiscenario on-spot applications.

Research Progresses in Microstructure Designs of Flexible Pressure Sensors

Flexible electronic technology is one of the research hotspots, and numerous wearable devices have been widely used in our daily life. As an important part of wearable devices, flexible sensors can effectively detect various stimuli related to specific environments or biological species, having a very bright development prospect. Therefore, there has been lots of studies devoted to developing high-performance flexible pressure sensors. In addition to developing a variety of materials with excellent performances, the microstructure designs of materials can also effectively improve the performances of sensors, which has brought new ideas to scientists and attracted their attention increasingly. This paper will summarize the flexible pressure sensors based on material microstructure designs in recent years. The paper will mainly discuss the processing methods and characteristics of various sensors with different microstructures, and compare the advantages, disadvantages, and application scenarios of them. At the same time, the main application fields of flexible pressure sensors based on microstructure designs will be listed, and their future development and challenges will be discussed.

Differentiation of Multiple Mechanical Stimuli by a Flexible Sensor Using a Dual-Interdigital-Electrode Layout for Bodily Kinesthetic Identification

Human bodily kinesthetic sensing is generally complicated and ever-changing due to the diversity of body deformation as well as the complexity of mechanical stimulus, which is different from the unidirectional mechanical motion. So, there exists a huge challenge for current flexible sensors to accurately differentiate and identify what kind of external mechanical stimulus is exerted via analyzing digital signals. Here, we report a flexible dual-interdigital-electrode sensor (FDES) that consists of two interdigital electrodes and a highly pressure-sensitive porous conductive sponge. The FDES can precisely identify multiple mechanical stimuli, e.g., pressing, positive bending, negative bending, X-direction stretching, and Y-direction stretching, and convert them into corresponding current variation signals. Moreover, the FDES exhibits other exceptional properties, such as high sensitivity, stretchability, large measurement range, and outstanding stability, accompanied by simple structural design and low-cost processing simultaneously. Additionally, our FDES successfully identifies various complex activities of the human body, which lays a foundation for the further development of multimode flexible sensors.