ENHANCING HUMAN–ROBOT SYNERGY USING EMOTION RECOGNITION
Human–Robot Interaction, Emotion Recognition, Deep Learning, Affective Computing, Social Robotics, CNN-LSTM, Intelligent Robotics
Human–Robot Interaction (HRI) has progressed from industrial automation toward socially intelligent collaboration where robots operate alongside humans in dynamic environments. Despite advancements in robotics and artificial intelligence, most robotic systems remain emotionally unaware, limiting their ability to interact naturally with humans. Emotional understanding is essential for effective communication, cooperation, and trust formation in shared human–robot environments.
This research proposes a comprehensive multimodal emotion recognition framework aimed at enhancing human–robot synergy through adaptive emotional intelligence. The proposed system integrates facial expression analysis, speech emotion recognition, and contextual behavioral modeling using deep learning techniques. A hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture is developed to capture spatial and temporal emotional cues. Multimodal fusion enables robust emotion classification under real-world variations such as lighting changes, speech noise, and user diversity.
Public datasets including FER2013, AffectNet, and RAVDESS are employed for training and validation. The robotic system dynamically modifies interaction behavior based on detected emotional states, enabling empathetic responses and improved collaboration efficiency. Experimental analysis demonstrates significant improvements in recognition accuracy and interaction effectiveness compared with conventional task-oriented robotic systems.
The study contributes a scalable architecture for affect-aware robotics and establishes a pathway toward emotionally intelligent autonomous systems suitable for healthcare, assistive technology, education, and collaborative industry applications.
Registration ID: IJVRA_701138 Published ID: IJVRA2603289
"ENHANCING HUMAN–ROBOT SYNERGY USING EMOTION RECOGNITION", IJVRA - International Journal of Versatile Research and Analysis (www.IJVRA.org), ISSN:2984-8903, Vol.4, Issue 3, page no.227-237, March-2026, Available :https://ijpub.org/ijvra/papers/IJVRA2603289.pdf
Paper Reg. ID: IJVRA_701138
Published Paper Id: IJVRA2603289
Research Area: Other area not in list
Country: Palwal, Haryana, India
ISSN: 2984-8903 | IMPACT FACTOR: 9.12 Calculated By Google Scholar | ESTD YEAR: 2023
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Publisher: IJVRA (IJ Publication) Janvi Wave
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