With the modernization of traditional Chinese medicine (TCM), creating devices to digitalize aspects of pulse diagnosis has proved to be challenging. The currently available pulse detection devices usually rely on external pressure devices, which are either bulky or poorly integrated, hindering their practical application. In this work, we propose an innovative wearable active pressure three-channel pulse monitoring device based on TCM pulse diagnosis methods. It combines a flexible pressure sensor array, flexible airbag array, active pressure control unit, advanced machine learning approach, and a companion mobile application for human–computer interaction. Due to the high sensitivity (460.1 kPa⁻¹), high linearity (R² > 0.999) and flexibility of the flexible pressure sensors, the device can accurately simulate finger pressure to collect pulse waves (Cun, Guan, and Chi) at different external pressures on the wrist. In addition, by measuring the change in pulse wave amplitude at different pressures, an individual’s blood pressure status can be successfully predicted. This enables truly wearable, actively pressurized, continuous wireless dynamic monitoring of wrist pulse health. The innovative and integrated design of this pulse monitoring platform could provide a new paradigm for digitizing aspects of TCM and other smart healthcare systems.

Tongue diagnosis is an important part of the diagnostic process in traditional Chinese medicine (TCM). It primarily relies on the expertise and experience of TCM practitioners in identifying tongue features, which are subjective and unstable. We proposed a tongue feature classification framework based on convolutional neural networks to reduce the differences in diagnoses among TCM practitioners. Initially, we used our self-designed instrument to capture 482 tongue photos and created 11 data sets based on different features. Then, the tongue segmentation task was completed using an upgraded facial landmark detection method and UNET. Finally, we used ResNet34 as the backbone to extract features from the tongue photos and classify them. Experimental results show that our framework has excellent results with an overall accuracy of over 86 percent and is particularly sensitive to the corresponding feature regions, and thus it could assist TCM practitioners in making more accurate diagnoses.

Soft piezocapacitive sensor, owing to its simple assembly and low power consumption, draws intensive interests for physiological monitoring. However, crosstalk between proximity sensing units and limited mechanically stretching suppressed its practical application for conventional capacitive sensor array. Here we report a stretchable iontronic pressure sensor (SIPS) array with low proximate crosstalk for epidermal monitoring. Benefiting from electric double layer (EDL) capacitance effect between robust aramid nanofiber (ANF) /MXene (Ti3C2Tx) composite electrode and ionic film, the SIPS exhibits a high sensitivity up to 521.69 kPa−1, low limit of detection (0.22 Pa), broad linear sensing range (200 kPa) and rapid response time (17 ms) as well as long-term stable working durability for 10,000 cycles. The serpentine island bridge structure enables the SIPS array to stretch up to 40%, allowing pulse and grip monitoring of a bent wrist and finger, respectively, through processing circuits. This demonstrates the promising application prospects in vital physiological monitoring.

In recent years, flexible pressure sensing arrays applied in medical monitoring, human-machine interaction, and the Internet of Things have received a lot of attention for their excellent performance. Epidermal sensing arrays can enable the sensing of physiological information, pressure, and other information such as haptics, providing new avenues for the development of wearable devices. This paper reviews the recent research progress on epidermal flexible pressure sensing arrays. Firstly, the fantastic performance materials currently used to prepare flexible pressure sensing arrays are outlined in terms of substrate layer, electrode layer, and sensitive layer. In addition, the general fabrication processes of the materials are summarized, including three-dimensional (3D) printing, screen printing, and laser engraving. Subsequently, the electrode layer structures and sensitive layer microstructures used to further improve the performance design of sensing arrays are discussed based on the limitations of the materials. Furthermore, we present recent advances in the application of fantastic-performance epidermal flexible pressure sensing arrays and their integration with back-end circuits. Finally, the potential challenges and development prospects of flexible pressure sensing arrays are discussed in a comprehensive manner.

The emerging field of flexible tactile sensing systems, equipped with multi-physical tactile sensing capabilities, holds vast potential across diverse domains such as medical monitoring, robotics, and human–computer interaction. In response to the prevailing challenges associated with the limited integration and sensitivity of flexible tactile sensors, this paper introduces a versatile tactile sensing system capable of concurrently monitoring temperature and pressure. The temperature sensor employs carbon nanotube/graphene conductive paste as its sensitive material, while the pressure sensor integrates an ionic gel containing boron nitride as its sensitive layer. Through the application of cost-effective screen printing technology, we have successfully manufactured a flexible dual-mode sensor with exceptional performance, featuring high sensitivity (804.27 ${\mathrm{kPa}}^{-1}$), a broad response range (50 kPa), rapid response time (17 ms), and relaxation time (34 ms), alongside exceptional durability over 5000 cycles. Furthermore, the resistance temperature coefficient of the sensor within the temperature range of 12.5 °C to 93.7 °C is −0.17% °C⁻¹. The designed flexible dual-mode tactile sensing system enables the real-time detection of pressure and temperature information, presenting an innovative approach to electronic skin with multi-physical tactile sensing capabilities.

The long-term monitoring of respiratory status is crucial for the prevention and diagnosis of respiratory diseases. However, existing continuous respiratory monitoring devices are typically bulky and require either chest strapping or proximity to the nasal area, which compromises user comfort and may disrupt the monitoring process. To overcome these challenges, we have developed a flexible, attachable, lightweight, and miniaturized system designed for extended wear on the wrist. This system incorporates signal acquisition circuitry, a mobile client, and a deep neural network, facilitating long-term respiratory monitoring. Specifically, we fabricated a highly sensitive (11,847.24 kPa⁻¹) flexible pressure sensor using a screen printing process, which is capable of functioning beyond 70,000 cycles. Additionally, we engineered a bidirectional long short-term memory (BiLSTM) neural network, enhanced with a residual module, to classify various respiratory states including slow, normal, fast, and simulated breathing. The system achieved a dataset classification accuracy exceeding 99.5%. We have successfully demonstrated a stable, cost-effective, and durable respiratory sensor system that can quantitatively collect and store respiratory data for individuals and groups. This system holds potential for everyday monitoring of physiological signals and healthcare applications.