Citation

Aalam, Zoobiya and Aziz, Saman and Lew, Kai Liang and Lee, Chia Shyan (2025) Real-Time Emotion Detection Using Artificial Intelligence: A Review. International Journal on Robotics, Automation and Sciences, 7 (1). pp. 104-110. ISSN 2682-860X

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Abstract

The integration of artificial intelligence (AI) in emotion recognition has significantly transformed human-computer interaction and revolutionized fields such as medicine, education, and entertainment. This paper reviews 30 papers on the detection of emotional signs through various biometric inputs, including electroencephalography (EEG), electrocardiography (ECG), facial expressions, and speech patterns. Despite advancements in AI-driven emotion recognition systems, challenges persist, particularly in data variability, computational inefficiency, and ethical dilemmas associated with privacy, security, and algorithmic bias. Recent innovations in feature extraction techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have enhanced the precision of emotional state recognition across multiple input channels. The transition to edge computing has further enabled real-time processing with low latency, facilitating integration into wearable devices and IoT ecosystems. Multimodal systems, which leverage data sources such as physiological signals, facial expressions, and speech, show great promise but face challenges related to inclusivity and system fragility. To address these issues, the study recommends for robust training datasets, ethical guidelines, and hardware optimizations. Incorporating contextual information and accounting for individual differences can improve recognition accuracy and user trust. However, ethical concerns remain critical, emphasizing the need for strict standards of privacy, security, and equitable access to ensure AI emotion recognition systems are trustworthy and inclusive. Overall, this paper highlights the potential of AI-driven emotion recognition systems while underscoring the importance of continuous research to address technical and ethical challenges, paving the way for broader applications in pattern recognition, cognitive studies, and specialized tools.

Item Type:	Article
Uncontrolled Keywords:	Emotion Recognition And Detection, Multimodal Systems, Deep Learning, Affective Computing, Ethical Ai
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	26 Jun 2025 01:29
Last Modified:	26 Jun 2025 01:29
URII:	http://shdl.mmu.edu.my/id/eprint/14071

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