Adaptive user interfaces for wearable medical devices using deep Q-learning and Golden Jackal Optimization

Abstract

Wearable medical devices offer continuous health monitoring but often rely on static user interfaces that do not adjust to individual user needs. This lack of adaptability presents accessibility challenges, especially for older adults and users with limited tech proficiency. To address this, we propose an adaptive user interface powered by reinforcement learning to personalize navigation flow, button placement, and notification timing based on real-time user behavior. Our system uses a deep Q-learning (DQL) model enhanced with the Golden Jackal Optimization (GJO) algorithm for improved convergence and performance. Usability testing was conducted to evaluate the adaptive interface against traditional static designs. The proposed DQL-GJO model demonstrated the fastest convergence, requiring only 45 epochs, compared to 70 for standard DQL and 48–62 for other hybrid models. It also achieved the lowest task completion time (TCT) at 82 s, the lowest error rate (ER) at 9.9%, and the highest user satisfaction (US) at 78%. These improvements suggest that the GJO-enhanced model not only accelerates training efficiency but also delivers superior user experience in practical use.