Wearable Strain Sensors with Aligned Macro Carbon Cracks Using a Two-Dimensional Triaxial-Braided Fabric Structure for Monitoring Human Health

Multi-Layer Polyurethane-Fiber-Prepared Entangled Strain Sensor with Tunable Sensitivity and Working Range for Human Motion Detection

The entanglement of fibers can form physical and topological structures, with the resulting bending and stretching strains causing localized changes in pressure. In this study, a multi-layer polyurethane-fiber-prepared (MPF) sensor was developed by coating the CNT/PU sensing layer on the outside of an elastic electrode through a wet-film method. The entangled topology of two MPFs was utilized to convert the stretching strain into localized pressure at the contact area, enabling the perception of stretching strain. The influence of coating mechanical properties and surface structure on strain sensing performance was investigated. A force regulator was introduced to regulate the mechanical properties of the entangled topology of MPF. By modifying the thickness and length proportion of the force regulator, the sensitivity factor and sensitivity range of the sensor could be controlled, achieving a high sensitivity factor of up to 127.74 and a sensitivity range of up to 58%. Eight sensors were integrated into a sensor array and integrated into a dance costume, successfully monitoring the multi-axis motion of the dancer's lumbar spine. This provides a new approach for wearable biomechanical sensors.

Long-Short-Term-Memory-Based Deep Stacked Sequence-to-Sequence Autoencoder for Health Prediction of Industrial Workers in Closed Environments Based on Wearable Devices

To reduce the risks and challenges faced by frontline workers in confined workspaces, accurate real-time health monitoring of their vital signs is essential for improving safety and productivity and preventing accidents. Machine-learning-based data-driven methods have shown promise in extracting valuable information from complex monitoring data. However, practical industrial settings still struggle with the data collection difficulties and low prediction accuracy of machine learning models due to the complex work environment. To tackle these challenges, a novel approach called a long short-term memory (LSTM)-based deep stacked sequence-to-sequence autoencoder is proposed for predicting the health status of workers in confined spaces. The first step involves implementing a wireless data acquisition system using edge-cloud platforms. Smart wearable devices are used to collect data from multiple sources, like temperature, heart rate, and pressure. These comprehensive data provide insights into the workers' health status within the closed space of a manufacturing factory. Next, a hybrid model combining deep learning and support vector machine (SVM) is constructed for anomaly detection. The LSTM-based deep stacked sequence-to-sequence autoencoder is specifically designed to learn deep discriminative features from the time-series data by reconstructing the input data and thus generating fused deep features. These features are then fed into a one-class SVM, enabling accurate recognition of workers' health status. The effectiveness and superiority of the proposed approach are demonstrated through comparisons with other existing approaches.

Seamless Weft Knit Vest with Integrated Needle Sensing Zone for Monitoring Shoulder Movement: A First Methodological Study

The integration of textile-based flexible sensors and electronic devices has accelerated the development of wearable textiles for posture monitoring. The complexity of the processes required to create a complete monitoring product is currently reflected in three main areas. The first is the sensor production process, which is complex. Second, the integration of the sensor into the garment requires gluing or stitching. Finally, the production of the base garment requires cutting and sewing. These processes deteriorate the user experience and hinder the commercial mass production of wearable textiles. In this paper, we knitted a one-piece seamless knitted vest (OSKV) utilizing the one-piece seamless knitting technique and positioned an embedded needle sensing zone (EHSZ) with good textile properties and electrical performance for monitoring human shoulder activity. The EHSZ was knitted together with the OSKV, eliminating the need for an integration process. The EHSZ exhibited good sensitivity (GF = 2.23), low hysteresis (0.29 s), a large stretch range (200%), and excellent stability (over 300 cycles), satisfying the requirement to capture a wide range of deformation signals caused by human shoulder movements. The OSKV described the common vest process structure without the stitching process. Furthermore, OSKV fulfilled the demand for seamless and trace-free monitoring while effortlessly and aesthetically satisfying the knitting efficiency of commercial garments.

Carbon-Based Textile Sensors for Physiological-Signal Monitoring

As the focus on physical health increases, the market demand for flexible wearable sensors increases. Textiles combined with sensitive materials and electronic circuits can form flexible, breathable high-performance sensors for physiological-signal monitoring. Carbon-based materials such as graphene, carbon nanotubes (CNTs), and carbon black (CB) have been widely utilized in the development of flexible wearable sensors due to their high electrical conductivity, low toxicity, low mass density, and easy functionalization. This review provides an overview of recent advancements in carbon-based flexible textile sensors, highlighting the development, properties, and applications of graphene, CNTs, and CB for flexible textile sensors. The physiological signals that can be monitored by carbon-based textile sensors include electrocardiogram (ECG), human body movement, pulse and respiration, body temperature, and tactile perception. We categorize and describe carbon-based textile sensors based on the physiological signals they monitor. Finally, we discuss the current challenges associated with carbon-based textile sensors and explore the future direction of textile sensors for monitoring physiological signals.

Crack-Based Core-Sheath Fiber Strain Sensors with an Ultralow Detection Limit and an Ultrawide Working Range

With the booming development of flexible wearable sensing devices, flexible stretchable strain sensors with crack structure and high sensitivity have been widely concerned. However, the narrow sensing range has been hindering the development of crack-based strain sensors. In addition, the existence of the crack structure may reduce the interface compatibility between the elastic matrix and the sensing material. Herein, to overcome these problems, integrated core-sheath fibers were prepared by coaxial wet spinning with partially added carbon nanotube sensing materials in thermoplastic polyurethane elastic materials. Due to the superior interface compatibility and the change in the conductive path during stretching, the fiber strain sensor exhibits excellent durability (5000 tensile cycles), high sensitivity (>10⁴), large stretchability (500%), a low detection limit (0.01%), and a fast response time of ∼60 ms. Based on these outstanding strain sensing performances, the fiber sensor is demonstrated to detect subtle strain changes (e.g., pulse wave and swallowing) and large strain changes (e.g., finger joint and wrist movement) in real time. Moreover, the fabric sensor woven with the core-sheath fibers has an excellent performance in wrist bending angle detection, and the smart gloves based on the fabric sensors also show exceptional recognition ability as a wireless sign language translation device. This integrated strategy may provide prospective opportunities to develop highly sensitive strain sensors with durable deformation and a wide detection range.

Ultrasensitive, Highly Stable, and Flexible Strain Sensor Inspired by Nature

For advanced flexible strain sensors, it is not difficult to achieve high sensitivity only. However, integrating high sensitivity, high stability, and high durability into one sensor still remains a great challenge. Fortunately, natural creatures with diversified excellent performances have given us a lot of ready-made solutions. Here, scorpion and spiderweb are selected as coupling bionic prototypes, which are famous for their ultrasensitive sensing capacity and excellent structural durability, respectively. Based on that, a bioinspired strain sensor is successfully fabricated. The results demonstrate that the bioinspired strain sensor has a sensitivity of 940.5 in the strain range of 0-1.5% and a sensitivity of 2742.3 between 1.5 and 2.5%. Meantime, this sensor with a spiderweb-like reticular structure has a great improvement in stability and durability. Specifically, the sensor exhibits excellent stability during bending and stretching cycles over 80,000 times. Moreover, the response time and recovery time of the sensor are 169 and 195 ms, respectively. Besides, the sensor also has functions such as vibrating frequency identification due to its low hysteresis. Based on the excellent performance, the sensor can be applied to monitor human body motions serving as wearable electronics.

Intelligent and highly sensitive strain sensor based on indium tin oxide micromesh with a high crack density

Cracks play an important role in strain sensors. However, a systematic analysis of how cracks influence the strain sensors has not been proposed. In this work, an intelligent and highly sensitive strain sensor based on indium tin oxide (ITO)/polyurethane (PU) micromesh is realized. The micromesh has good skin compatibility, water vapor permeability, and stability. Due to the color of the ITO/PU micromesh, it can be invisible on the skin. Based on the fragility of ITO, the density and resistance of cracks in the micromesh are greatly improved. Therefore, the ITO/PU micromesh strain sensor (IMSS) has an ultrahigh gauge factor (744.3). In addition, a finite element model based on four resistance layers is proposed to explain the performance of the IMSS and show the importance of high-density cracks. Compared with other strain sensors based on low-density cracks, the IMSS based on high-density cracks has larger sensitivity and better linearity. Physiological signals, such as respiration, pulse, and joint motion, can be monitored using the IMSS self-fixed on the skin. Finally, an invisible and artificial throat has been realized by combining the IMSS with a convolutional neural network algorithm. The artificial throat can translate the throat vibrations of the tester automatically with an accuracy of 86.5%. This work has great potential in health care and language function reconstruction.

Network crack-based high performance stretchable strain sensors for human activity and healthcare monitoring

In this study, high performance wearable and stretchable strain sensors are developed for human activity and healthcare monitoring, and wearable electronics.

Millefeuille-Inspired Thermal Interface Materials based on Double Self-Assembly Technique for Efficient Microelectronic Cooling and Electromagnetic Interference Shielding

Owing to the increasing power density of miniaturized and high-frequency electronic devices, flexible thermal interface materials (TIMs) with the electromagnetic interference (EMI) shielding property are in urgent demand to maintain the system performance and reliability. Recently, carbon-based TIMs receive considerable attention due to the ultrahigh intrinsic thermal conductivity (TC). However, the large-scale production of such TIMs is restricted by some technical difficulties, such as production-induced defects of graphite sheets, poor microstructure architecture within the matrix, and nonnegligible interfacial thermal resistance result from the strong phono scattering. In this work, inspired by the structure and production process of millefeuille cakes, a unique double self-assembly strategy for fabricating ultrahigh thermal conductive TIMs with superior EMI shielding performance is demonstrated. The percolating and oriented multilayered microstructure enables the TIM to exhibit an ultrahigh in-plane TC of 233.67 W m^-1 K^-1 together with an outstanding EMI shielding effectiveness of 79.0 dB (at 12.4 GHz). In the TIM evaluation system, a nearly 45 °C decrease is obtained by this TIM when compared to the commercial material. The obtained TIM achieves the desired balance between thermal conduction and EMI shielding performance, indicating broad prospects in the fields of military applications and next-generation thermal management systems.

Low-Temperature Wearable Strain Sensor Based on a Silver Nanowires/Graphene Composite with a Near-Zero Temperature Coefficient of Resistance

Currently, the exploration of wearable strain sensors that can work under subzero temperatures while simultaneously possessing anti-interference capability toward temperature is still a grand challenge. Herein, we present a low-temperature wearable strain sensor that is constructed via the incorporation of a Ag nanowires/graphene (Ag NWs/G) composite into the polydimethylsiloxane (PDMS) polymer. The Ag NWs/G/PDMS strain sensor exhibits promising flexibility at a very low temperature (-40 °C), outstanding fatigue resistance with low hysteresis energy, and near-zero temperature coefficient of resistance (TCR). The Ag NWs/G/PDMS strain sensor shows excellent sensing performance under subzero temperatures with a very high gauge factor of 9156 under a strain of >36%, accompanied by a noninterference characteristic to temperature (-40 to 20 °C). The Ag NWs/G/PDMS strain sensor also demonstrates the feasibility of monitoring various human movements such as finger bending, arm waving, wrist rotation, and knee bending under both room temperature and low-temperature conditions. This work initiates a new promising strategy to construct next-generation wearable strain sensors that can work stably and effectively under very low temperatures.