Sensor analysis and fusion for detecting human states and affect is a challenging open problem in ubiquitous computing research, as well as a popular milestone for the automobile industry. The use of driver assistance systems has become increasingly popular due to advances in Artificial Intelligence, with the aim of improving road safety and reducing the number of accidents caused by human error. Despite their great potential, the deployment of such technologies is still at infant stage, especially when considering the driver’s affective state, which can greatly impact driving performance. This project aims to address this issue by developing systems and improving the performance of affective state detection in driving with the use of multimodal biometric sensor information, such as EDA, ECG, PPG, and respiration.
@article{avramidis2025estimating,title={Estimating Markers of Driving Stress through Multimodal Physiological Monitoring},author={Avramidis, Kleanthis and Zhou, Emily and Feng, Tiantian and Shishavan, Hossein Hamidi and Severgnini, Frederico Marcolino Quintao and Lohan, Danny J and Schmalenberg, Paul and Dede, Ercan M and Narayanan, Shrikanth},journal={arXiv preprint arXiv:2507.14146},year={2025},}
2024
Journal
Scaling Representation Learning from Ubiquitous ECG with State-Space Models
@article{avramidis2023scaling,title={Scaling Representation Learning from Ubiquitous ECG with State-Space Models},author={Avramidis, Kleanthis and Kunc, Dominika and Perz, Bartosz and Adsul, Kranti and Feng, Tiantian and Kazienko, Przemys{\l}aw and Saganowski, Stanis{\l}aw and Narayanan, Shrikanth},journal={IEEE Journal of Biomedical and Health Informatics},year={2024},}
2023
ICASSP 2023
Multimodal Estimation of Change Points of Physiological Arousal in Drivers
@inproceedings{Kleanthis-AmbientAI-Wkshp-ICASSP23,author={Avramidis, Kleanthis and Feng, Tiantian and Bose, Digbalay and Narayanan, Shrikanth},booktitle={Ambient AI Workshop: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},title={Multimodal Estimation of Change Points of Physiological Arousal in Drivers},year={2023},pages={1-5},}