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Wearable sensors for human physiological monitoring have attracted tremendous interest from researchers in recent years. However, most of the research involved simple trials without any significant analytical algorithms. This study provides a way of recognizing human motion by combining textile stretch sensors based on single-walled carbon nanotubes (SWCNTs) and spandex fabric (PET/SP) and machine learning algorithms in a realistic application. In the study, the performance of the system will be evaluated by identification rate and accuracy of the motion standardized. This research aims to provide a realistic motion sensing wearable product without unnecessary heavy and uncomfortable electronic devices.

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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.

Electronic textiles, also known as smart textiles or smart fabrics, are one of the best form factors that enable electronics to be embedded in them, presenting physical flexibility and sizes that cannot be achieved with other existing electronic manufacturing techniques. As part of smart textiles, e-sensors for human movement monitoring have attracted tremendous interest from researchers in recent years. Although there have been outstanding developments, smart e-textile sensors still present significant challenges in sensitivity, accuracy, durability, and manufacturing efficiency. This study proposes a two-step approach (from structure layers and shape) to actively enhance the performance of e-textile strain sensors and improve manufacturing ability for the industry. Indeed, the fabricated strain sensors based on the silver paste/single-walled carbon nanotube (SWCNT) layers and buffer cutting lines have fast response time, low hysteresis, and are six times more sensitive than SWCNT sensors alone. The e-textile sensors are integrated on a glove for monitoring the angle of finger motions. Interestingly, by attaching the sensor to the skin of the neck, the pharynx motions when speaking, coughing, and swallowing exhibited obvious and consistent signals. This research highlights the effect of the shapes and structures of e-textile strain sensors in the operation of a wearable e-textile system. This work also is intended as a starting point that will shape the standardization of strain fabric sensors in different applications.

Today, e-textiles have become a fundamental trend in wearable devices. Fabric pressure sensors, as a part of e-textiles, have also received much interest from many researchers all over the world. However, most of the pressure sensors are made of electronic fibers and composed of many layers, including an intermediate layer for sensing the pressure. This paper proposes the model of a single layer pressure sensor with electrodes and conductive fibers intertwined. The plan dimensions of the fabricated sensors are 14 x 14 mm, and the thickness is 0.4 mm. The whole area of the sensor is the pressure-sensitive point. As expected, results demonstrate an electrical resistance change from 283 Ω at the unload pressure to 158 Ω at the load pressure. Besides, sensors have a fast response time (50 ms) and small hysteresis (5.5%). The hysteresis will increase according to the pressure and loading distance, but the change of sensor loading distance is very small. Moreover, the single-layer pressure sensors also show high durability under many working cycles (20,000 cycles) or washing times (50 times). The single-layer pressure sensor is very thin and more flexible than the multi-layer pressure sensor. The structure of this sensor is also expected to bring great benefits to wearable technology in the future.

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.

Flexible strain sensors are receiving a great deal of interest owing to their prospective applications in monitoring various human activities. Among various efforts to enhance the sensitivity of strain sensors, pre-crack generation has been well explored for elastic polymers but rarely on textile substrates. Herein, a highly sensitive textile-based strain sensor was fabricated via a dip-coat-stretch approach: a polyester woven elastic band was dipped into ink containing single-walled carbon nanotubes coated with silver paste and pre-stretched to generate prebuilt cracks on the surface. Our sensor demonstrated outstanding sensitivity (a gauge factor of up to 3550 within a strain range of 1.5–5%), high stability and durability, and low hysteresis. The high performance of this sensor is attributable to the excellent elasticity and woven structure of the fabric substrate, effectively generating and propagating the prebuilt cracks. The strain sensor integrated into firefighting gloves detected detailed finger angles and cyclic finger motions, demonstrating its capability for subtle human motion monitoring. It is also noteworthy that this novel strategy is a very quick, straightforward, and scalable method of fabricating strain sensors, which is extremely beneficial for practical applications.