Author:
Yuan Gao
Affiliation:
College of Arts and Sciences, University of Washington, 4801 24th Ave NE APT 6130, Seattle, U.S.A.
Keyword(s):
Emotion Recognition, Human-Robot Interaction, EEG-Based Emotion Detection, Multimodal Fusion, Empathy in Robotics, Ethical AI.
Abstract:
This paper explores recent advancements in emotion recognition techniques within Human-Robot Interaction (HRI), focusing on the evolution of perception-based, electroencephalogram (EEG)-based, and multimodal fusion approaches. Emotion recognition has become essential for enhancing the emotional intelligence of robots, which can now better detect and respond to human emotions, particularly within service and healthcare applications. Key contributions of this study include the integration of deep learning models, such as Convolutional Neural Networks (CNNs) and Transformer-based architectures, which have shown significant improvements in real-time emotion recognition accuracy. Additionally, the paper discusses the integration of hardware innovations that optimize responsiveness, enabling robots to provide a more empathetic and supportive experience. This research also examines the role of empathy within social robotics and its ethical implications, covering data privacy, user consent,
and the need for fair, unbiased artificial intelligence (AI). By emphasizing the importance of regulatory frameworks, this study outlines the future of emotion-aware AI in safe, ethical, and effective human-robot collaboration.
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