DOI: 10.1155/2023/5079256 ISSN:

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

Jianhui Gao, Zhi Li, Zhong Chen
  • Electrical and Electronic Engineering
  • Instrumentation
  • Control and Systems Engineering

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.

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