Nonverbal Interaction

Nonverbal interaction research focuses on understanding how humans communicate through non-linguistic cues like facial expressions, gestures, and body language, aiming to computationally model and interpret these complex signals within social contexts. Current research employs multimodal machine learning approaches, including convolutional neural networks, recurrent neural networks (like LSTMs), and hypergraph models, to analyze large datasets of human interactions, often incorporating techniques like transfer learning and attention mechanisms. This work has implications for diverse fields, including education (assessing teaching effectiveness), human-robot interaction (improving robot responsiveness), and mental health (detecting behavioral indicators of mental states), by providing objective and quantitative measures of nonverbal communication.