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EMOTIONAL RECOGNITION IN SOCIALLY INTERACTIVE ROBOTS: A COMPREHENSIVE REVIEW


Article Information

Title: EMOTIONAL RECOGNITION IN SOCIALLY INTERACTIVE ROBOTS: A COMPREHENSIVE REVIEW

Authors: Aryan Ahmed, Hashir Alvi, M.Hamza Khalid, Fawad Naseer

Journal: Spectrum of Engineering Sciences

HEC Recognition History
Category From To
Y 2024-10-01 2025-12-31

Publisher: Sociology Educational Nexus Research Institute

Country: Pakistan

Year: 2025

Volume: 3

Issue: 8

Language: en

Keywords: Machine Learning (ML)Artificial Intelligence (AI)Social robotsHuman-Robot InteractionEmotion-Aware RobotEmotional RecognitionRobot Companion

Categories

Abstract

The study explores the evolving landscape of Human-Robot Interaction (HRI) within workplace environments, examining the intricate relationship between humans and robots in professional settings. As artificial intelligence (AI), particularly generative AI, becomes increasingly integrated into the workplace, understanding HRI dynamics is paramount for businesses, society, and ethical considerations. This paper synthesizes current research to provide a comprehensive overview of key trends, challenges, and future research agendas in workplace HRI, examining how advancements in AI, such as natural language processing and adaptive interaction, are shaping human-robot collaboration, communication, and coexistence in professional contexts. The review highlights the growing importance of trust, anthropomorphism, and social intelligence in facilitating effective HRI, drawing upon insights from psychology, computer science, and sociology. It addresses the implications of generative AI technologies like ChatGPT in creating more intuitive and human-like interactions, while acknowledging the ethical dilemmas and potential pitfalls associated with these technologies. Furthermore, the paper discusses how workplace HRI can enhance employee well-being, improve operational efficiency, and foster innovation. By identifying gaps in current research, this review aims to guide future studies, focusing on areas such as long-term user adaptation, AI decision-making interpretability, and the development of robust ethical frameworks for responsible AI deployment in professional environments.


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