Accessible Gesture-Driven Augmented Reality Interaction System
This work addresses accessibility for motor-impaired users in AR, though it appears incremental as it builds on existing gesture recognition and interface optimization methods.
The study tackled the inaccessibility of augmented reality (AR) for users with motor impairments by proposing a gesture-based interaction system using deep learning, resulting in a 20% improvement in task completion efficiency and a 25% increase in user satisfaction compared to baseline systems.
Augmented reality (AR) offers immersive interaction but remains inaccessible for users with motor impairments or limited dexterity due to reliance on precise input methods. This study proposes a gesture-based interaction system for AR environments, leveraging deep learning to recognize hand and body gestures from wearable sensors and cameras, adapting interfaces to user capabilities. The system employs vision transformers (ViTs), temporal convolutional networks (TCNs), and graph attention networks (GATs) for gesture processing, with federated learning ensuring privacy-preserving model training across diverse users. Reinforcement learning optimizes interface elements like menu layouts and interaction modes. Experiments demonstrate a 20% improvement in task completion efficiency and a 25% increase in user satisfaction for motor-impaired users compared to baseline AR systems. This approach enhances AR accessibility and scalability. Keywords: Deep learning, Federated learning, Gesture recognition, Augmented reality, Accessibility, Human-computer interaction