Artificial Intelligence for data modeling in triboelectric nanogenerators

Abstract

This review presents a comprehensive study on the integration of Artificial Intelligence (AI) with Triboelectric Nanogenerators (TENGs), emphasizing their convergence in advancing real-time sensing, signal interpretation, and self-powered systems. Over 20 experimental implementations are analyzed, combining AI models such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks with TENGs across multiple operational modes including contact-separation, lateral sliding, and freestanding configurations. Application cases discussed include AI-powered triboelectric smart socks achieving 96.67 % activity recognition accuracy, soft robotic grippers with 98.1 % object identification precision, and wearable pulse sensors for continuous blood pressure monitoring using personalized machine learning algorithms. Quantitative analyses of machine learning frameworks are presented, with CNNs and ANNs demonstrating up to 99.32 % accuracy in TENG signal processing tasks. Deep learning techniques are shown to enhance noise filtering, feature extraction, and adaptive feedback, transforming TENGs into intelligent platforms for healthcare, robotics, IoT systems, and smart environments. The review also addresses key challenges such as data variability, environmental robustness, and algorithmic scalability, and future directions in hybrid energy systems, adaptive algorithms, and cross-disciplinary collaboration for sustainable, intelligent sensing technologies.