Can Physiological Sensors Predict Psychological State?

This summer, computer science concentrators Sindy Liu’18, Eseosa Asiruwa’18, Mitchel Herman’19 and Matthew Goon’18 are doing research with machine learning on outputs from various sensors. The research project is directed by Stephen Harper Kirner Chair of Computer Science Stuart Hirshfield. Data from physiological sensors, including heart rate and electrodermal activity (EDA), functional Near Infrared Spectroscopy (fNIRS), electroencephalogram (EEG), and electrocardiogram (ECG), are primarily used to detect electrical activity in the brain or heart, or electrical conductance of the skin. Current research indicates that such signals change depending on the person’s psychological state, and can be used to predict traits such as feelings, attention and awareness. Previous research has collected and processed physiological data from different devices in different manners. This year, student researchers are attempting to collect, devise a common format for, and run data…


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