Enhancing Human-Machine Interface Design Through Visual Perception Optimization Algorithms

Effective human-machine interaction (HMI) relies on intuitive interfaces that align with human perceptual capabilities. As mobile apps and intelligent systems evolve, optimizing these interfaces becomes critical for enhancing user experience. This paper presents a novel approach to improving HMI design through the optimization of visual perception intensity. By analyzing the visual characteristics of human cone cells, the study divides visual perception levels and establishes visual communication indicators as optimization targets. A mathematical model is developed, incorporating Kalman filters for prediction and adjustment. The results demonstrate a 25.11% improvement in the visual communication index, validating the model's ability to optimize interface brightness and better align with human visual perception. This work provides a significant contribution to the design of adaptive interfaces, advancing the field of human-machine interaction with practical applications in mobile and digital technologies.