Integration of Flexible Touch Panels and Machine Learning: Applications and Techniques
Jiayue Zheng, Yongyang Chen, Zhiyi Wu, Yuanyu Wang, Linlin Wang
Abstract
The rapid advancement of mobile devices and human–machine interaction technologies has ushered in a new era for flexible touch panels as a novel input interface. This article reviews the historical evolution and technical progress of flexible touch panel technologies, from rudimentary single‐point touch to sophisticated grid‐free touch systems. Additionally, the working principles and mechanisms that underpin these advanced systems, including capacitive, resistive, piezoelectric, and triboelectric nanogenerator technologies, are explored. Following this, the integration of machine learning methods into these panels is discussed, offering new avenues for enhancing user experience and expanding functional capabilities. Various machine learning algorithms such as support vector machines, artificial neural networks, convolutional neural networks, and k ‐nearest neighbors are examined for their potential applications in touch panel technologies. Finally, the challenges and prospects for the application of flexible touch panels fused with machine learning are discussed.