Research on Emotional Interaction AI Algorithm Optimization and Human Robot Collaboration Models for Elderly Care Robots

Abstract

This paper conducts an in-depth study on emotional interaction artificial intelligence algorithm optimization and human-robot collaboration models specifically designed for elderly care robots. In the context of a rapidly aging global society, the demand for comprehensive elderly care services continues to increase exponentially. Consequently, advanced emotional recognition and empathetic interaction capabilities have become key factors in improving the overall service quality and acceptance of care robots. This study proposes a novel, deep learning-based emotion recognition algorithm that achieves highly accurate identification of complex emotional states in older adults. This is accomplished through the integrated, multimodal analysis of speech dialogue, facial expressions, and continuous physiological signals. With regard to human-robot collaboration models, the study systematically examines collaborative working mechanisms between intelligent robotic assistants and human care staff. Furthermore, it proposes a dynamic adjustment strategy based on real-time emotional feedback to substantially improve collaboration efficiency and user satisfaction. Experimental results demonstrate that the optimized emotional interaction algorithm significantly improves emotion recognition accuracy and computational response speed. Additionally, the newly proposed collaboration model effectively reduces the physical and psychological workload pressure on human care staff while elevating the overall quality of elderly care services. The core innovations of this study lie in the development of a context-aware human-robot interaction mechanism and an adaptive task allocation model, which collectively provide novel technical solutions to enhance the intelligence, empathy, and responsiveness of modern elderly care services.