The recent advances in wearable electronics and intelligent human-machine interface systems have garnered great interests in electromechanical sensors, which can measure and quantify physical stimuli. Among different types of electromechanical sensors, piezoresistive sensors have been extensively investigated due to the excellent sensitivity, simple construction, and durability. Especially, there have been remarkable developments of flexible and stretchable piezoresistive sensors for wearable devices by investigating novel material/structural strategies to obtain highly sensitive piezoresistive sensors with skin-like flexibility. Here, we give a comprehensive overview of the recent progress in flexible and stretchable piezoresistive sensors and their applications. Based on the material composition and structural characteristics, the piezoresistive sensors are categorized into three types—conductive polymeric composite, porous conductive material, and architected conductive material. Subsequently, we have summarized their transduction mechanisms, fabrication processes, sensing performances, and applications. Finally, we have discussed current challenges and future opportunities for piezoresistive sensors.

The recent advances in wearable sensors and intelligent human–machine interfaces have sparked a great many interests in conductive fibers owing to their high conductivity, light weight, good flexibility, and durability. As one of the most impressive materials for wearable sensors, conductive fibers can be made from a variety of raw sources via diverse preparation strategies. Herein, to offer a comprehensive understanding of conductive fibers, we present an overview of the recent progress in the materials, the preparation strategies, and the wearable sensor applications related. Firstly, the three types of conductive fibers, including metal-based, carbon-based, and polymer-based, are summarized in terms of their principal material composition. Then, various preparation strategies of conductive fibers are established. Next, the primary wearable sensors made of conductive fibers are illustrated in detail. Finally, a robust outlook on conductive fibers and their wearable sensor applications are addressed.

Pressure sensitivity and wide range are two crucial features of flexible electromechanical sensors for applications in the next‐generation of intelligent electronics, such as wearable healthcare monitors and soft human–machine interfaces. Conventional pressure sensors have a narrow pressure range (<10 kPa) and complex fabrication processes, which significantly hinder their extensive applications. A facile laser‐engraving method is proposed to fabricate a flexible multiwalled‐carbon‐nanotube (MWCNTs)/poly(dimethylsiloxane) (PDMS) composite‐based piezoresistive sensor with hierarchical microstructures. Herein, the nonstandard‐circular feature and Gaussian distributed facula of a laser spot are utilized to produce the middle‐level porous microdome upon the bottom‐level cylinder microcolumn array, while the top‐level tentacle‐like conical micropillars are produced by vertically rotating the acrylic mold during the laser engraving process. This novel hierarchical microstructure endows the proposed piezoresistive sensor with orders‐of‐magnitude of higher sensitivity (≈35.51 kPa⁻¹) than that of other reported electromechanical sensors and a more extensive pressure sensing range up to 23 kPa. Moreover, the detection limit of the sensor is down to 2 Pa, which makes it a desirable candidate for monitoring subtle pressure. The sensor is successfully applied to distinguish the syllables of each pronounced word, detect movements of the human wrist, and monitor radial arterial pulse, thus demonstrating its promising applications in wearable electronics.

Global aging leads to a surge in neurological diseases. Quantitative gait analysis for the early detection of neurological diseases can effectively reduce the impact of the diseases. Recently, extensive research has focused on gait-abnormality-recognition algorithms using a single type of portable sensor. However, these studies are limited by the sensor’s type and the task specificity, constraining the widespread application of quantitative gait recognition. In this study, we propose a multimodal gait-abnormality-recognition framework based on a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) network. The as-established framework effectively addresses the challenges arising from smooth data interference and lengthy time series by employing an adaptive sliding window technique. Then, we convert the time series into time–frequency plots to capture the characteristic variations in different abnormality gaits and achieve a unified representation of the multiple data types. This makes our signal processing method adaptable to several types of sensors. Additionally, we use a pre-trained Deep Convolutional Neural Network (DCNN) for feature extraction, and the consequently established CNN-BiLSTM network can achieve high-accuracy recognition by fusing and classifying the multi-sensor input data. To validate the proposed method, we conducted diversified experiments to recognize the gait abnormalities caused by different neuropathic diseases, such as amyotrophic lateral sclerosis (ALS), Parkinson’s disease (PD), and Huntington’s disease (HD). In the PDgait dataset, the framework achieved an accuracy of 98.89% in the classification of Parkinson’s disease severity, surpassing DCLSTM’s 96.71%. Moreover, the recognition accuracy of ALS, PD, and HD on the PDgait dataset was 100%, 96.97%, and 95.43% respectively, surpassing the majority of previously reported methods. These experimental results strongly demonstrate the potential of the proposed multimodal framework for gait abnormality identification. Due to the advantages of the framework, such as its suitability for different types of sensors and fewer training parameters, it is more suitable for gait monitoring in daily life and the customization of medical rehabilitation schedules, which will help more patients alleviate the harm caused by their diseases.

Because of the dilemma that the current industrial Cu enhancement methods lead to a significant decline in conductivity and ductility, Cu matrix composites reinforced by oriented multi-walled carbon nanotubes (MWCNTs) were prepared through sintering, hot extrusion, and cold drawing. Before sintering, Ni, Cu, and Ni&Cu coatings were electroless plated on MWCNTs as the intermediate transition layer, and then they were mixed with Cu powder through a nitrogen bubbling assisted ultrasonic process. By analyzing the composition, microstructure, and formation mechanism of the interface between MWCNTs and the matrix, the influence and mechanism of the interface on the mechanical properties, conductivity, and ductility of the composites were explored. The results indicated that MWCNTs maintained a highly dispersed and highly consistent orientation in the Cu matrix. The coating on Ni@CNT was the densest, continuous, and complete. The Ni@CNTs/Cu composite had the greatest effect, while the Cu composite reinforced by MWCNT without coating had the smallest reduction in elongation and conductivity. The comprehensive performance of the Cu@CNTs/Cu composite was the most balanced, with an ultimate tensile strength that reached 373 MPa, while the ductility and conductivity were not excessively reduced. The axial electrical and thermal conductivity were 79.9 IACS % (International Annealed Copper Standard) and 376 W/mK, respectively.