Machine learning–assisted triboelectric nanogenerator technology for intelligent sports
Minglan Ji, Zhen Wang, Jiamin Wu, Lijun Huang, Mingli Zheng, Gang Cheng, Huaihong Cai, Jianjun Luo, Haibo Zhou, Zhong Lin Wang
Abstract
The rapid development of internet of things, big data, and artificial intelligence is propelling sports science into a data-driven era, demanding real-time, multidimensional athletic performance monitoring. Triboelectric nanogenerators (TENGs) have demonstrated exceptional potential in intelligent sports. However, the complexity and volume of TENG-generated data pose challenges for manual analysis. Machine learning (ML), with strengths in pattern recognition and adaptive processing, provides a powerful solution to enhance TENG-based sensing signal interpretation. This review systematically explores the integration of ML and TENG technology for intelligent sports. First, the fundamental theory and basic knowledge of TENGs are introduced, highlighting their versatility in sports sensing systems. Subsequently, a comprehensive overview of ML models for TENG signal analysis is discussed. Recent advancements of ML-assisted TENG-based intelligent sports applications, including sports training evaluation, sports health monitoring, and virtual/augmented reality sports, are then highlighted. Last, current challenges and future prospects of TENG-based intelligent sports systems are discussed.