Multimoadal Sensor Monitoring


Virtual Theatre


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.
Collaborators
  • 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
  • Publications
  • 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 Interspeech 2015: Towards a better understanding of the most important biosignal. Accepted.