dc.creator | Liu, Changchun | |
dc.date.accessioned | 2020-08-21T20:56:41Z | |
dc.date.available | 2011-01-20 | |
dc.date.issued | 2009-01-20 | |
dc.identifier.uri | https://etd.library.vanderbilt.edu/etd-01202009-121746 | |
dc.identifier.uri | http://hdl.handle.net/1803/10458 | |
dc.description.abstract | Recent advances in robotics and intelligent systems are expected to usher in a new era where the need for machines to “understand” humans becomes increasingly important. It should permit more meaningful and natural human-machine interaction (HMI) when a robot/computer can detect the affective cues of the person it is working with. The objective of this work is to investigate the following hypotheses for achieving an affect-sensitive HMI: (i) It is possible to detect the affective states of interest by using multiple indices derived from physiological signals in real-time; (ii) Such affective cues can be integrated within a machine's control architecture to make it capable of responding to them appropriately; and (iii) Such affect-sensitive systems are expected to improve the overall human-machine interaction experience. In this work, a systematic comparison of the strengths and weaknesses of machine learning methods was performed when they were employed for the physiology-based affect recognition. The impacts of the affect-sensitive closed-loop interaction were investigated in both human-robot interaction (HRI) and human-computer interaction (HCI) contexts. Furthermore, in response to the growing need for developing robot/computer assisted autism intervention systems for children with autism spectrum disorder (ASD), physiology-based affective modeling and adaptation methods were investigated for this specific population. Finally, physiology-based affective modeling using active learning for children with ASD was discussed. | |
dc.format.mimetype | application/pdf | |
dc.subject | Human machine interaction | |
dc.subject | physiology | |
dc.subject | emotion | |
dc.subject | Autism in children -- Treatment | |
dc.subject | Affect (Psychology) | |
dc.subject | Emotions -- Physiological aspects | |
dc.subject | Human-computer interaction | |
dc.subject | human robot interaction | |
dc.subject | affect computing | |
dc.subject | Robotics -- Human factors | |
dc.title | Physiology-based affect recognition and adaptation in human-machine interaction | |
dc.type | dissertation | |
dc.contributor.committeeMember | George E. Cook | |
dc.contributor.committeeMember | Mitch Wilkes | |
dc.contributor.committeeMember | Richard Shiavi | |
dc.contributor.committeeMember | Zachary E. Warren | |
dc.type.material | text | |
thesis.degree.name | PHD | |
thesis.degree.level | dissertation | |
thesis.degree.discipline | Electrical Engineering | |
thesis.degree.grantor | Vanderbilt University | |
local.embargo.terms | 2011-01-20 | |
local.embargo.lift | 2011-01-20 | |
dc.contributor.committeeChair | Nilanjan Sarkar | |