Papers
6
Total Citations
193
H-Index
4
About
Irvin Dongo is a leading researcher at the intersection of social robotics and affective computing, whose work is fundamentally shaping how machines understand and express human emotion. His primary research areas include multimodal emotion detection, human-robot interaction (HRI), and the application of natural language processing (NLP) and ontologies to robotic perception. Dongo’s most significant contributions lie in developing adaptive, multimodal architectures that allow social robots to recognize emotions through a fusion of facial expressions, gestures, and voice, moving beyond single-modality limitations. His landmark paper, "Adaptive Multimodal Emotion Detection Architecture for Social Robots" (2022), has garnered 88 citations, while his earlier work on leveraging NLP Transformers and emotion ontologies for text-based detection (2021) has accumulated 83 citations, underscoring their foundational impact on the field. Beyond emotion recognition, Dongo has explored object detection and recognition for service robots in specialized environments like museums. His recent work on evaluating robot emotion expressions (2024) continues to push the boundaries of creating more natural and effective HRI, solidifying his reputation as a key innovator in making robots more emotionally intelligent and user-friendly.
Research Focus
Key Achievements
Top Papers
- 1Adaptive Multimodal Emotion Detection Architecture for Social Robots88 citations · 2022
- 2
- 3Evaluation of Robot Emotion Expressions for Human–Robot Interaction10 citations · 2024
- 4
- 5Multimodal Emotional Understanding in Robotics4 citations · 2022
- 6