Wearable Device Styling Design Integrating Deep Q-Learning and Morphosemantic Analysis

Abstract

This paper proposes an intelligent styling-design model for wearable devices by integrating Deep Q-Learning reinforcement learning with morphosemantic analysis, aiming to solve the problems of inaccurate user-demand mapping, insufficient design innovation, and weak human–machine interaction adaptability. Based on morphosemantic theory, reinforcement learning principles, and ergonomic guidelines, the core design dimensions of wearable device styling, including morphological features, color schemes, material selection, and interaction layout, are defined together with user-demand evaluation metrics. A multidimensional dataset, WED-2024, is then constructed from mainstream wearable brands, user surveys, and design cases, covering five product categories and eight user satisfaction levels. A technical framework of “requirement semantic mapping-DQN intelligent generation-morphological semantic optimization” is designed. Morphosemantic analysis translates user requirements into design features, while the exploration-exploitation mechanism of DQN generates diverse design proposals. A semantic consistency loss function is introduced to improve the alignment between generated solutions and user needs. Comparative experiments evaluate design satisfaction, innovation, and human–machine adaptability, providing a technical reference for wearable sensing terminals and intelligent interaction design in wireless communication environments.