Fatigue-Aware Adaptive Interfaces for Wearable Devices Using Deep Learning

TL;DR

This paper presents a deep learning-based system that adapts wearable device interfaces in real-time to reduce user fatigue, improving engagement and satisfaction during prolonged use.

Contribution

It introduces a novel multimodal deep learning framework that dynamically adjusts interface elements based on physiological data to mitigate fatigue.

Findings

18% reduction in cognitive load

22% improvement in user satisfaction

Effective adaptation during prolonged tasks

Abstract

Wearable devices, such as smartwatches and head-mounted displays, are increasingly used for prolonged tasks like remote learning and work, but sustained interaction often leads to user fatigue, reducing efficiency and engagement. This study proposes a fatigue-aware adaptive interface system for wearable devices that leverages deep learning to analyze physiological data (e.g., heart rate, eye movement) and dynamically adjust interface elements to mitigate cognitive load. The system employs multimodal learning to process physiological and contextual inputs and reinforcement learning to optimize interface features like text size, notification frequency, and visual contrast. Experimental results show a 18% reduction in cognitive load and a 22% improvement in user satisfaction compared to static interfaces, particularly for users engaged in prolonged tasks. This approach enhances accessibility and usability in wearable computing environments.