A self-powered corrugated paper structure triboelectric sensor with deep learning for table tennis motion...

Area of Science:

• Materials Science
• Triboelectric Nanogenerators
• Artificial Intelligence

Background:

• Intelligent sports equipment is crucial for performance analysis and personalized training.
• Triboelectric nanogenerators (TENGs) offer potential for energy harvesting and motion sensing applications.

Purpose of the Study:

• To develop a corrugated paper-based triboelectric nanogenerator (CP-TENG) for mechanical energy harvesting and table tennis motion sensing.
• To integrate AI for signal recognition and player identification.

Main Methods:

• Fabrication of a CP-TENG using PVDF/PZT composite films and a roughened copper layer.
• Electrical characterization of the CP-TENG's output performance.
• Integration of a CP-TENG sensor array into a table tennis racket.
• Application of AI algorithms for motion tracking and player identification.

Main Results:

• The CP-TENG demonstrated enhanced electrical outputs compared to pure PVDF devices (∼36% V_OC, ∼37% I_SC, ∼29% Q_SC).
• Interconnected CP-TENG units achieved stable outputs of ∼46.8 V, ∼15.1 μA, and ∼34.9 nC.
• AI-assisted analysis of sensor data enabled reliable volunteer identification with 94.5% accuracy.

Conclusions:

• The developed CP-TENG effectively harvests mechanical energy and senses motion.
• AI-powered analysis of CP-TENG data facilitates intelligent sports monitoring and player identification.
• This technology shows promise for advanced, self-powered sports equipment.