Post-surgical treatments of the human throat often require continuous monitoring of diverse vital and muscle activities. However, wireless, continuous monitoring and analysis of these activities directly from the throat skin have not been developed. Here, we report the design and validation of a fully integrated standalone stretchable device platform that provides wireless measurements and machine learning-based analysis of diverse vibrations and muscle electrical activities from the throat. We demonstrate that the modified composite hydrogel with low contact impedance and reduced adhesion provides high-quality long-term monitoring of local muscle electrical signals. We show that the integrated triaxial broad-band accelerometer also measures large body movements and subtle physiological activities/vibrations. We find that the combined data processed by a 2D-like sequential feature extractor with fully connected neurons facilitates the classification of various motion/speech features at a high accuracy of over 90%, which adapts to the data with noise from motion artifacts or the data from new human subjects. The resulting standalone stretchable device with wireless monitoring and machine learning-based processing capabilities paves the way to design and apply wearable skin-interfaced systems for the remote monitoring and treatment evaluation of various diseases.

To realize a hyperconnected smart society with high productivity, advances in flexible sensing technology are highly needed. Nowadays, flexible sensing technology has witnessed improvements in both the hardware performances of sensor devices and the data processing capabilities of the device’s software. Significant research efforts have been devoted to improving materials, sensing mechanism, and configurations of flexible sensing systems in a quest to fulfill the requirements of future technology. Meanwhile, advanced data analysis methods are being developed to extract useful information from increasingly complicated data collected by a single sensor or network of sensors. Machine learning (ML) as an important branch of artificial intelligence can efficiently handle such complex data, which can be multi-dimensional and multi-faceted, thus providing a powerful tool for easy interpretation of sensing data. In this review, the fundamental working mechanisms and common types of flexible mechanical sensors are firstly presented. Then how ML-assisted data interpretation improves the applications of flexible mechanical sensors and other closely-related sensors in various areas is elaborated, which includes health monitoring, human–machine interfaces, object/surface recognition, pressure prediction, and human posture/motion identification. Finally, the advantages, challenges, and future perspectives associated with the fusion of flexible mechanical sensing technology and ML algorithms are discussed. These will give significant insights to enable the advancement of next-generation artificial flexible mechanical sensing.

Highlights: The iontronic pressure sensor achieved an ultrahigh sensitivity (S_min > 200 kPa⁻¹, S_max > 45,000 kPa⁻¹). The iontronic pressure sensor exhibited a broad sensing range of over 1.4 MPa. Pseudocapacitive iontronic pressure sensor using MXene was proposed. Flexible pressure sensors are unprecedentedly studied on monitoring human physical activities and robotics. Simultaneously, improving the response sensitivity and sensing range of flexible pressure sensors is a great challenge, which hinders the devices’ practical application. Targeting this obstacle, we developed a Ti₃C₂T_x-derived iontronic pressure sensor (TIPS) by taking the advantages of the high intercalation pseudocapacitance under high pressure and rationally designed structural configuration. TIPS achieved an ultrahigh sensitivity (S_min > 200 kPa⁻¹, S_max > 45,000 kPa⁻¹) in a broad sensing range of over 1.4 MPa and low limit of detection of 20 Pa as well as stable long-term working durability for 10,000 cycles. The practical application of TIPS in physical activity monitoring and flexible robot manifested its versatile potential. This study provides a demonstration for exploring pseudocapacitive materials for building flexible iontronic sensors with ultrahigh sensitivity and sensing range to advance the development of high-performance wearable electronics.

3D printing-based supercapacitors have been extensively explored, yet the rigid rheological requirement for corresponding ink preparation significantly limits the manufacturing of true 3D architecture in achieving superior energy storage. We proposed the stereolithographic technique to fabricate the metallic composite lattices with octet-truss arrangement by using electroless plating and engineering the 3D hierarchically porous graphene onto the scaffolds to build the hierarchically cellular lattices in quasi-solid supercapacitor application. The supercapacitor device that is composed of composite lattices span several pore size orders from nm to mm holds promising behavior on the areal capacitance (57.75 mF cm⁻²), rate capability (70% retention, 2–40 mA cm⁻²), and long lifespan (96% after 5000 cycles), as well as superior energy density of 0.008 mWh cm⁻², which are comparable to the state-of-the-art carbon-based supercapacitor. By synergistically combining this facile stereolithographic 3D printing technology with the hierarchically porous graphene architecture, we provide a novel route of manufacturing energy storage device as well as new insight into building other high-performance functional electronics.

The laser-engraved method was introduced to fabricate the electrode for the sensor. The sensor showed a wide linear working range, superior sensitivity, and fast response time and also exhibited excellent viability in a wet situation. Wireless integrated network sensors successfully monitored the health states. Developing flexible sensors with high working performance holds intense interest for diverse applications in leveraging the Internet-of-things (IoT) infrastructures. For flexible piezoresistive sensors, traditionally most efforts are focused on tailoring the sensing materials to enhance the contact resistance variation for improving the sensitivity and working range, and it, however, remains challenging to simultaneously achieve flexible sensor with a linear working range over a high-pressure region (> 100 kPa) and keep a reliable sensitivity. Herein, we devised a laser-engraved silver-coated fabric as “soft” sensor electrode material to markedly advance the flexible sensor’s linear working range to a level of 800 kPa with a high sensitivity of 6.4 kPa⁻¹ yet a fast response time of only 4 ms as well as long-time durability, which was rarely reported before. The integrated sensor successfully routed the wireless signal of pulse rate to the portable smartphone, further demonstrating its potential as a reliable electronic. Along with the rationally building the electrode instead of merely focusing on sensing materials capable of significantly improving the sensor’s performance, we expect that this design concept and sensor system could potentially pave the way for developing more advanced wearable electronics in the future. The online version of this article (10.1007/s40820-020-00498-y) contains supplementary material, which is available to authorized users.