Roadmap to Human–Machine Interaction through Triboelectric Nanogenerator and Machine Learning Convergence
Anand Babu, Dipankar Mandal
Abstract
Embark on a journey to unlock the pivotal roadmap for seamlessly integrating triboelectric nanogenerators (TENGs) with advanced machine learning algorithms. This review article endeavors to present a comprehensive strategy for the integration of machine learning algorithms with TENGs specifically tailored for gesture monitoring applications. The primary objective is to outline a meticulous methodology for the seamless fusion of TENG technology and machine learning techniques by elucidating the key tools for data collection, robust analysis, potential challenges, and compelling case studies that highlight the tangible applications of this fusion across diverse domains. This review not only delves into the underlying principles of TENG but also explores the fundamental tenets of machine learning, making it accessible to a wide readership, from novices to experts. The goal is to facilitate the effective implementation of this integrated framework, enabling the development of more efficient and sophisticated gesture monitoring solutions for elevating the human–machine interaction to greater heights.