This project, developing a highly integrated and modular system for affective sensing research, aims at designing, developing, verifying, and disseminating an instrumentation setup that can be used to sense, record, and identify physiological changes that signal affective shifts relevant to human-computer interaction (HCI). A large number of biological signal sensors will be integrated for the purpose of developing HCI with the ability to respond to the state of the user's autonomic nervous system (including user emotion and affect, and states related to exercise and health). Affective computing implies that a computer system should be able to assess the emotional state of the subject, i.e., perform affective sensing based on real-time monitoring of the physiological expression of the user's affective state. Non-invasive/unobtrusive measurements in this monitoring process ultimately yield enhancements in HCI. Physiological manifestations of sympathetic activation associated with affective shifts in a highly integrated sensory platform may reveal inconspicuous but relevant features that could be overlooked when the signals are observed in isolation. The work involves five sets of experiments:-DSP for affective sensing,-Real-time measurement of eye gaze tracking,-Analysis of the relationship between exercise and blood volume, -EEG as a way to assess the quality of HCI, and-Autonomic nervous system monitoring as a way to assess HCI.Broader Impact: This affective sensing system can be used as an evaluation platform toward the design of the next generation of human computer interfaces that would improve access and functional capabilities of persons with disabilities. The development of the instrument will impact the training and motivation of future professionals and researchers. This work contributes in the creation of new initiatives to engage students in an institution serving a large number of minority students; thus strengthening the student pipeline towards graduate degrees.
