Date of Award


Document Type


Degree Name




First Advisor

Mohammad H. Mahoor


Autism, Eye-gaze, Hidden Markov Model, NAO Robot, Robot-Human Interatcion, Variable-order Markov Model


Children with Autism Spectrum Disorders (ASDs) experience deficits in verbal and nonverbal communication skills including motor control, emotional facial expressions, and eye gaze attention. In this thesis, we focus on studying the feasibility and effectiveness of using a social robot, called NAO, at modeling and improving the social responses and behaviors of children with autism. In our investigation, we designed and developed two protocols to fulfill this mission. Since eye contact and gaze responses are important non-verbal cues in human's social communication and as the majority of individuals with ASD have difficulties regulating their gaze responses, in this thesis we have mostly focused on this area.

In Protocol 1 (eye gaze duration and shifting frequency are analyzed in this protocol), we designed two social games (i.e. NAO Spy and Find the Suspect) and recruited 21 subjects (i.e. 14 ASD and seven Typically Developing (TD) children) ages between 7-17 years old to interact with NAO. All sessions were recorded using cameras and the videos were used for analysis. In particular, we manually annotated the eye gaze direction of children (i.e. gaze averted `0' or gaze at robot `1') in every frame of the videos within two social contexts (i.e. child speaking and child listening). Gaze fixation and gaze shifting frequency are analyzed, where both patterns are significantly improved or changed (more than half of the participants increased the eye contact duration time and decrease the eye gaze shifting during both games). The results confirms that the TD group has more gaze fixation as they are listening (71%) than while they are speaking (37%). However there is no significant difference between the average gaze fixations of ASD group.

Besides using the statistical measures (i.e. gaze fixation and shifting), we statistically modeled the gaze responses of both groups (TD and ASD) using Markov models (e.g. Hidden Markov Model (HMM) and Variable-order Markov Model (VMM)). Using Markov based modeling allows us to analyze the sequence of gaze direction of ASD and TD groups for two social conversational sessions (Child Speaking and Listening). The results of our experiments show that for the `Child Speaking' segments, HMM can distinguish and recognize the differences of gaze patterns of TD and ASD groups accurately (79%). In addition, to evaluate the effect of history of eye gaze in the gaze responses, the VMM technique was employed to model the effects of different length of sequential data. The results of VMM demonstrate that, in general, the first order system (VMM with order D=1) can reliably represent the differences between the gaze patterns of TD and ASD group. Besides that, the experimental results confirm that VMM is more reliable and accurate for modeling the gaze responses of "Child Listening" sessions than the "Child Speaking" one.

Protocol 2 contains five sub-sessions targeted intervention of different social skills: verbal communication, joint attention, eye gaze attention, facial expressions recognition/imitation. The objective of this protocol is to provide intervention sessions based on the needs of children diagnosed with ASD. Therefore each participant attended in three times of baseline sessions for evaluate his/her existing social skill and behavioral response, when the study began. In this protocol the behavioral responses of every child is recorded in each intervention session where feedbacks are focused on improving their social skills if they lack one. For example if they are not good at recognizing facial expression, we give them feedback on how every facial expression looks like and ask them to recognize them correctly while we do not feedback on other social skills. Our experimental results show that customizing the human-robot interaction would improve the social skills of children with ASD.


Recieved from ProQuest

Rights holder

Huanghao Feng

File size

111 p.

File format





Engineering, Psychology