Enhancing Tactile Internet Experiences through Control Mechanisms and Predictive AI

This research focuses on developing a simulation and assessment system for Internet Tactile AI, addressing the need for reliable evaluation methods in predictive models within this domain. The term "Internet Tactile" refers to touch-sensitive devices and applications interconnected in our digital ecosystem. The system aims to simulate and assess AI models' performance in predicting and responding to tactile stimuli in Internet environments. Key components include realistic tactile simulations, diverse datasets, and advanced assessment metrics. Simulation involves creating lifelike scenarios of tactile interactions online, coupled with AI models trained on extensive datasets to predict and analyze user responses. For all parameters and axes, it appears that the MAE, RMSE, and ESS values are generally lower for PD compared to MPC. This suggests that, in this context, PD may be more accurate in predicting positions compared to MPC.