High-performance flexible pressure sensors are highly desirable in health monitoring, robotic tactile, and artificial intelligence. Construction of microstructures in dielectrics and electrodes is the dominating approach to improving the performance of capacitive pressure sensors. Herein, we have demonstrated a novel three-dimensional microconformal graphene electrode for ultrasensitive and tunable flexible capacitive pressure sensors. Because the fabrication process is controllable, the morphologies of the graphene that is perfectly conformal with the electrode are controllable consequently. Multiscale morphologies ranging from a few nanometers to hundreds of nanometers, even to tens of micrometers, have been systematically investigated, and the high-performance capacitive pressure sensor with high sensitivity (3.19 kPa–1), fast response (30 ms), ultralow detection limit (1 mg), tunable-sensitivity, high flexibility, and high stability was obtained. Furthermore, an ultrasensitivity of 7.68 kPa–1 was successfully achieved via symmetric double microconformal graphene electrodes. The finite element analysis indicates that the microstructured graphene electrode can enhance large deformation and thus effectively improve the sensitivity. Additionally, the proposed pressure sensors are demonstrated with practical applications including insect crawling detection, wearable health monitoring, and force feedback of robot tactile sensing with a sensor array. The microconformal graphene may provide a new approach to fabricating controllable microstructured electrodes to enhance the performance of capacitive pressure sensors and has great potential for innovative applications in wearable health-monitoring devices, robot tactile systems, and human–machine interface systems.

By training the deep neural network model, the hidden features in Surface Electromyography(sEMG) signals can be extracted. The motion intention of the human can be predicted by analysis of sEMG. However, the models recently proposed by researchers often have a large number of parameters. Therefore, we designed a compact Convolution Neural Network (CNN) model, which not only improves the classification accuracy but also reduces the number of parameters in the model. Our proposed model was validated on the Ninapro DB5 Dataset and the Myo Dataset. The classification accuracy of gesture recognition achieved good results.

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