Real-Time Human Intention Recognition for Safe and Efficient Interaction in Assistive Robotic Platforms

Abstract

Human-robot interaction in assistive technologies has evolved significantly over the past decade with increasing focus on anticipatory computing paradigms. This paper presents a novel framework for real-time human intention recognition in assistive robotic platforms designed to support individuals with mobility impairments. The proposed system leverages multimodal sensor fusion and deep reinforcement learning to predict user intentions with minimal latency while maintaining high accuracy in dynamic environments. Our approach utilizes a hierarchical attention network that incorporates physiological signals, environmental context, and historical interaction patterns to achieve an overall prediction accuracy of 94.3\% with a latency of 47ms on standard hardware configurations. Experimental validation conducted across 37 participants with varying degrees of mobility impairments demonstrated significant improvements in task completion time (reduced by 28.7\%) and physical exertion (reduced by 32.1\%) compared to reactive assistance systems. Furthermore, our adaptive calibration algorithm allows for personalization that accommodates individual user preferences and capabilities, resulting in a 41.5\% improvement in user satisfaction metrics. This work addresses the critical challenge of intention-action gap in assistive robotics and establishes a foundation for intuitive human-robot collaboration in rehabilitation and daily living assistance scenarios.