Educational haptic platforms can leverage various modalities in order create effective interactive environments that can support embodied physical interactions. These platforms have the potential to leverage a student’s physical intuition to make abstract topics in physics, math, and other fields of science more concrete.
We develop machine learning models of affect using touch and biometrics (such as EEG signal, skin conductance, heart rate, etc) to support affective interaction with companion robots for health applications.