Dual-Mode Pressure Sensor Integrated with Deep Learning Algorithm for Joint State Monitoring in Tennis Motion

Abstract

The precise capture and identification of movement features are important for numerous scientific endeavors. In this work, we present a novel multimodal sensor, called the resistance/capacitance dual-mode (RCDM) sensor, which effectively differentiates between compression and stretchable strains during tennis motion; meanwhile, it can also accurately identify various joint movements. The proposed wearable device features a seamless design, comprising two separate components: a resistive part and a capacitive part. The resistive and capacitive components operate independently and utilize a resistance–capacitance mechanism to measure pressure and strain signals, respectively. The RCDM sensor demonstrates remarkable sensitivity to strains (GF = 7.84, 0%–140%) and exceptional linear sensitivity (S = 4.08 kPa−1) through capacitance. Utilizing machine learning algorithms, the sensor achieves a recognition rate of 97.21% in identifying various joint movement patterns. This advanced production method makes it feasible to manufacture the sensors on a large scale, offering tremendous potential for various applications, including tennis sports systems.