Sparse Electromagnetic-Compatibility Flexible Electrodes for High-Accuracy, Fast-Recognition Human–Computer Interfaces

Abstract Surface electromyography (sEMG) gesture recognition technology offers significant potential for enhancing human-computer interaction and rehabilitation engineering by providing an intuitive and noninvasive method of engagement. However, challenges such as electromagnetic interference (EMI), signal feature processing, and adaptability to individual variability remain prevalent. To address these issues, this paper presents a comprehensive framework that encompasses material selection, device design, feature selection algorithms, integration strategies, and system layout to create a skin-integrated electronic system functioning as a wireless control interface between humans and machines. This system features a flexible electrode that incorporates fractal design elements, serpentine structures, honeycomb configurations, and multilayer graphene, along with an efficient feature selection algorithm grounded in data dispersion principles. It is characterized by enhanced resistance to EMI, conformal attachment capabilities, rapid motion recognition, and high accuracy. Our work stands out with a dramatically higher shielding efficiency of 99.98% and a classification accuracy rate of 97.8%. This methodology significantly enhances the performance of sEMG-based human-computer interfaces, emphasizing rapid recognition speeds and high accuracy in real-time applications such as remote-control systems and virtual reality environments, thereby broadening its applicability across diverse contexts.