Multimoadal Sensor Monitoring
The long-term goal of the project is to develop a personal health system that – through the fusion of multi-modal sensor signals – enables continuous monitoring of personal cognitive load and early detection of the warning signs of stress as a health risk, so as to prevent the associated illness and other adverse events as well as reduce health-care cost.
Cecilia Ovesdotter Alm, Department of English, Rochester Institute of Technology
Reynold Bailey, Department of Computer Science, Rochester Institute of Technology
Linwei Wang, Golisano College of Computing and Information Sciences, Rochester Institute of Technology
Srinivas Sridharan, Department of Computer Science, Stevens Institute of Technology
Vasudev Bethamcherla, Dept of Computer Science, Rochester Institute of Technology
William Paul, Department of Computer Science, Rochester Institute of Technology
Ashley Edwards, SUNY Geneseo
Anthony Massicci, Onondaga Community College
Ashley A. Edwards, Anthony Massicci, Srinivas Sridharan, Joe Geigel, Linwei Wang, Reynold Bailey, and Cecilia Ovesdotter Alm. 2017.
Sensor-based Methodological Observations for Studying Online Learning.
In Proceedings of the 2017 ACM Workshop on Intelligent Interfaces for Ubiquitous and Smart Learning (SmartLearn '17). ACM, New York, NY, USA, 25-30.
Bethamcherla, Vasudev P., Paul, Will, Alm, Cecilia O., Bailey, Reynold, Geigel, Joe, Wang, Linwei, 2015. Face-speech sensor fusion for non-invasive stress detection. Proceedings of
1st Joint Conference on Facial Analysis, Animation & Auditory-Visual Speech Processing.
Paul, Will, Alm, Cecilia O., Bailey, Reynold, Geigel, Joe, Wang, Linwei
2015. Stressed out: What speech tells us about stress. Proceedings of
Towards a better understanding of the most important biosignal. Accepted.