Researchers use fixation identification algorithms to parse eye movement trajectories into a series of fixations and saccades, simplifying analyses and providing measures which may relate to cognition. The Distance Dispersion (I-DD) a widely-used elementary fixation identification algorithm. Yet the "optimality" properties of its most popular greedy implementation have not been described. This paper: (1) asks how "optimal" should be defined, and advances maximizing total fixation time and minimizing number of clusters as a definition; (2) asks whether the greedy implementation of I-DD is optimal, and shows that it is when no fixations are rejected for being too short; and (3) we show that when fixation time rejection criterion are enabled, the greedy algorithm is not optimal. We propose an O(n2) algorithm which is.

Far infrared thermography, which can be used to detect thermal radiation emitted by humans, has been used to detect physical disease, physiological changes relating to emotion, and polygraph testing, but has not been used for eye tracking. However, because the surface temperature of the cornea is colder than the limbus, it is theoretically possible to track corneal movements through thermal imaging. To explore the feasibility of thermal eye tracking, we invited 10 adults and tracked their corneal movements with passive thermal imaging at 60 Hz. We combined shape models of eyes with intensity threshold to segment the cornea from other parts of the eye in thermal images. We used an animation sequence as a calibration target for 5 point calibration/validation 5 times. Our results were compared to simultaneously collected data using an SR EyeLink eye tracker at 500 Hz, demonstrating the feasibility of eye tracking with thermal images. Blinking and breathing frequencies, which reflect the psychophysical status of the participants, were also robustly detected during thermal eye tracking.

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.

In their expanding role as tutors, home and healthcare assistants, robots must effectively interact with individuals of varying ability and temperament. Indeed, deploying robots in long-term social engagements will almost certainly require robots to reliably detect and adapt to changes in the demeanor of social partners to promote trust and more productive collaboration. However, the recognition of emotional state typically relies on the interpretation of very subtle cues, often varying from one person to the next. In addition, while facial expressions, body posture and features of speech have been used to detect affective changes, the robustness of these measures is often hindered by cultural and age differences. Recently, infrared thermography has shown promise in detecting guilt, fear and stress, indicating that it may be a viable sensing modality for improved human-robot interaction. In this study, we evaluated the efficacy of using a far infrared (FIR) camera for detecting robot-elicited affective response compared to video-elicited affective response by tracking thermal changes in five areas of the face. Further, we analyzed localized changes in the face to assess whether thermal and electrodermal responses to emotions elicited by traditional video techniques and by robots are similar. Finally, we performed principal component analysis to reduce the dimensionality of data and evaluated the performance using machine learning techniques for classifying thermal data by emotion state, resulting in a thermal classifier with a performance accuracy of 77.5%.

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.