<![CDATA[The integration of robotics into healthcare has the potential to revolutionize patient care and support for caregivers, particularly through assistive technologies. However, effective Human-Robot Interaction (HRI) remains a critical challenge, limiting the adaptability, trust, and usability of these systems. This study explores advanced methodologies to enhance HRI for assistive robots, focusing on improving emotional intelligence, adaptability, and user experience. Key innovations include the implementation of AI-powered emotion recognition systems, adaptive interaction models using reinforcement learning, and multimodal communication that combines speech, gestures, and visual cues. These features aim to create intuitive and empathetic robotic systems that can better understand and respond to diverse patient needs. The proposed framework is tested in simulated healthcare environments, evaluating its effectiveness through metrics like usability, trust, and patient outcomes. Preliminary findings indicate that enhancing HRI significantly improves patient engagement and reduces caregiver burden. By addressing ethical considerations and cultural sensitivities, this research contributes to the development of socially acceptable, technically advanced assistive technologies, paving the way for a more human-centered approach to robotics in healthcare.]]>
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