This paper modifies the DBSCAN algorithm to identify fixations and saccades. This method combines advantages from dispersion-based algorithms, such as resilience to noise and intuitive fixational structure, and from velocity-based algorithms, such as the ability to deal appropriately with smooth pursuit (SP) movements.

Robots are often employed to proactively engage children with Autism Spectrum Disorder (ASD) in well-defined physical or social activities to promote specific educational or therapeutic outcomes. However, much can also be learned by leveraging a robot's unique ability to objectively deliver stimuli in a consistent, repeatable way and record child-robot interactions that may be indicative of developmental ability and autism severity in this population. In this study, we elicited affective responses with an emotion-simulating robot and recorded child-robot interactions and child-other interactions during robot emotion states. This research makes two key contributions. First, we analyzed child-robot interactions and affective responses to an emotion-simulating robot to explore differences between the responses of typically developing children and children with Autism Spectrum Disorder (ASD). Next, we characterized play and affective responsivity and its connection to severity of autism symptoms using the Autism Diagnostic Observation Schedule (ADOS) calibrated severity scores. This preliminary work delivers a novel and robust robot-enabled technique for (1) differentiating child-robot interactions of a group of very young children with ASD (n=12) from a group of typically developing children (n=15) and, (2) characterizing within-group differences in play and affective response that may be associated with symptoms of autism severity.

This research employs an expressive robot to elicit affective response in young children and explore correlations between autonomously-detected play, affective response and developmental ability. In this study, we introduce a new, affective interface that combines sound, color, movement and context to simulate the expression of emotions. Our approach exploits social contingencies to emphasize the importance of situational cues in the proper interpretation of affective state. We studied a group of young children at various ages and stages of cognitive development, to: (1) evaluate the efficacy of using captured motion data to autonomously detect physical patterns of play while interacting with a robot, (2) examine relationships between physical play patterns and observed affective response and, (3) explore associations between developmental ability and play or affective response. This pilot study demonstrates that aggregate patterns of physical interaction with a robot are distinguishable through autonomous data collection. Further, statistical analyses demonstrates that developmental ability may be directly related to how a child interacts with and responds to an affective robot.

Atypical looking behaviors in neuropsychiatric conditions such as autism spectrum disorders (ASD) are not only a reflection of inherently abnormal neuropsychological processes, but also suggest that future access to observational learning opportunities may be limited. The work presented in this paper uses interactive eye tracking as a first step towards the development of automated tools that can help toddlers and young children with atypical visual attention learn to attend to social information in a more typical fashion. In our study, we designed an automated visual strategy training system that would redirect a viewers' attention to locations highly salient to the normative control group when the viewer drifted from those locations for a significant period of time. We evaluated our experimental technique on typically-developing adults, obtaining results that suggest that looking patterns can be altered to be more similar to those evidenced by a normative group of young children. Furthermore, these alterations appear be retained in post-training sessions when considering new presentations of videos participants had been trained upon, and, on more sensitive outcome measures based on integrated scanpath probabilities (heatmaps), seemed to generalize presentations not trained upon as well. The development of these techniques may provide a new model for modifying attentional biases not only in toddlers with ASD, but also in children affected by other neuropsychiatric conditions, and may thus lead to new therapeutic interventions as well as more efficacious methods for identifying the patterns associated with abnormal, attention-driven experience.

Most analytic approaches for eye-tracking data focus either on identification of fixations and saccades, or on estimating saliency properties. Analyzing both aspects of visual attention simultaneously provides a more comprehensive view of strategies used to process information. This work presents a method that incorporates both aspects in a unified Bayesian model to jointly estimate dynamic properties of scanpaths and a saliency map. Performance of the model is assessed on simulated data and on eye-tracking data from 15 children with autism spectrum disorder and 13 control children. Saliency differences between ASD and TD groups were found for both social and non-social images, but differences in dynamic gaze features were evident in only a subset of social images. These results are consistent with previous region-based analyses as well as previous fixation parameter models, suggesting that the new approach may provide synthesizing and statistical perspectives on eye-tracking analyses.

Fixation identification algorithms facilitate data comprehension and provide analytical convenience in eye-tracking analysis. However, current fixation algorithms for eye-tracking analysis are heavily dependent on parameter choices, leading to instabilities in results and incompleteness in reporting. This work examines the nature of human scanning patterns during complex scene viewing. We show that standard implementations of the commonly used distance-dispersion algorithm for fixation identification are functionally equivalent to greedy spatiotemporal tiling. We show that modeling the number of fixations as a function of tiling size leads to a measure of fractal dimensionality through box counting. We apply this technique to examine scale-free gaze behaviors in toddlers and adults looking at images of faces and blocks, as well as large number of adults looking at movies or static images. The distributional aspects of the number of fixations may suggest a fractal structure to gaze patterns in free scanning and imply that the incompleteness of standard algorithms may be due to the scale-free behaviors of the underlying scanning distributions. We discuss the nature of this hypothesis, its limitations, and offer directions for future work.

In this paper we evaluate several of the most popular algorithms for segmenting fixations from saccades by testing these algorithms on the scanning patterns of toddlers. We show that by changing the parameters of these algorithms we change the reported fixation durations in a systematic fashion. However, we also show how choices in analysis can lead to very different interpretations of the same eye-tracking data. Methods for reconciling the disparate results of different algorithms as well as suggestions for the use of fixation identification algorithms in analysis, are presented.