Emotional and Cognitive States Prediction Algorithm

Proposed prediction algorithm for human's emotional and cognitive state

Description: This research is to predict human’s emotional and cognitive states using physiological data (such as EEG, EMG, ECG, GSR) and behavior data (camera-based facial expression, body gestures, and speech). In order to estimate the states, we explore advanced machine learning technology, data mining, and signal processing to increase the accuracy rates. We will merge the predicted states by the proposed algorithm with swarm-robot controller to efficiently and safely interact with human in the real-world.

Publications:

1. Go-Eum Cha and Byung-Cheol Min, "Correlation between Unconscious Mouse Actions and Human Cognitive Workload", 2022 ACM CHI Conference on Human Factors in Computing Systems - Late-Breaking Work, New Orleans, LA, USA, April 30-May 6, 2022. (Paper, Video)
2. Wonse Jo, Robert Wilson, Jaeeun Kim, Steve McGuire, and Byung-Cheol Min, "Toward a Wearable Biosensor Ecosystem on ROS 2 for Real-time Human-Robot Interaction Systems", 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Workshop on HMRS 2021: Cognitive and Social Aspects of Human Multi-Robot Interaction, Prague, Czech Republic, Sep 27 – Oct 1, 2021. (Paper, Presentation, GitHub)
3. Wonse Jo, Shyam Sundar Kannan, Go-Eum Cha, Ahreum Lee, and Byung-Cheol Min, "ROS-based Framework for Monitoring Human and Robot Conditions in a Human-Multi-robot Team" arXiv preprint, arXiv:2006.03784, 2020. (Paper)
4. Wonse Jo, Shyam Sundar Kannan, Go-Eum Cha, Ahreum Lee, and Byung-Cheol Min, "ROSbag-based Multimodal Affective Dataset for Emotional and Cognitive States", 2020 IEEE International Conference on Systems, Man and Cybernetics (SMC), Toronto, Canada, 11-14 October, 2020. (Paper)