OASIS 2014 Abstracts


Full Papers
Paper Nr: 2
Title:

Typicality Degrees to Measure Relevance of the Physiological Signals - Assessing user’s Affective States

Authors:

Joseph Onderi Orero

Abstract: Physiological measures have a key advantage as they can provide an insight into human feelings that the subjects may not even be consciously aware of. However, modeling user affective states through pysiology still remains with critical questions especially on the relevant physiological measures for real-life emotionally intelligent applications. In this study, we propose the use of typicality degrees defined according to cognitive science and psychology principles to measure the relevance of the physiological features in characterizing user affective states. Thanks to the typicality degrees, we found consistent physiological characteristics for modeling user affective states.

Paper Nr: 3
Title:

What Could a Body Tell a Social Robot that It Does Not Know?

Authors:

Dennis Küster and Arvid Kappas

Abstract: Humans are extremely efficient in interacting with each other. They not only follow goals to exchange information, but modulate the interaction based on nonverbal cues, knowledge about situational context, and person information in real time. What comes so easy to humans poses a formidable challenge for artificial systems, such as social robots. Providing such systems with sophisticated sensor data that includes expressive behavior and physiological changes of their interaction partner holds much promise, but there is also reason to be skeptical. We will discuss issues of specificity and stability of responses with view to different levels of context.

Paper Nr: 4
Title:

Revealing Psychophysiology and Emotions through Thermal Infrared Imaging

Authors:

Arcangelo Merla

Abstract: Thermal infrared imaging has been proposed as a tool for the non-invasive and contact-less evaluation of vital signs, psychophysiological responses and states. Several applications have been so far developed in many diversified fields, like social and developmental psychology, psychometrics, human-computer interaction, continuous monitoring of vital signs, stress and, even, deception detection. Thermal infrared imaging has been poorly exploited in the field of human-robot interaction. Therefore, the state of the art of thermal infrared imaging in computational physiology and psychophysiology is discussed in order to provide insights about its potentialities and limits for human-robot interaction and applications with affective robots.

Paper Nr: 5
Title:

Physiological Signals in Driving Scenario - How Heart Rate and Skin Conductance Reveal Different Aspects of Driver’s Cognitive Load

Authors:

Thi-Hai-Ha Dang and Adriana Tapus

Abstract: Driver’s cognitive load has always been associated with the driver’s heart rate activity and his/her skin conductance activity. However, what aspects of cognitive load that these signals relate to have never been clearly studied. This paper presents our preliminary results about the relationship between the different physiological signals (heart rate and skin conductance) and the driver’s cognitive load. Via one experiment with simulated car driving environment and one experiment in real flying environment, our data suggests that subjects’ heart rate relates to the number of events to be processed by the human driver while the skin conductance relates to the novelty of the driving task. Given the small population involved in these experiments, tests on more subjects are planned and reported in the future.

Paper Nr: 6
Title:

Using Near Infrared Spectroscopy to Index Temporal Changes in Affect in Realistic Human-robot Interactions

Authors:

Megan Strait and Matthias Scheutz

Abstract: Recent work in HRI found that prefrontal hemodynamic activity correlated with participants’ aversions to certain robots. Using a combination of brain-based objective measures and survey-based subjective measures, it was shown that increasing the presence (co-located vs. remote interaction) and human-likeness of the robot engaged greater neural activity in the prefrontal cortex and severely decreased preferences for future interactions. The results of this study suggest that brain-based measures may be able to capture participants’ affective responses (aversion vs. affinity), and in a variety of interaction settings. However, the brain-based evidence of this work is limited to temporally-brief (6-second) post-interaction samples. Hence, it remains unknown whether such measures can capture affective responses over the course of the interactions (rather than post-hoc). Here we extend the previous analysis to look at changes in brain activity over the time course of more realistic human-robot interactions. In particular, we replicate the previous findings, and moreover find qualitative evidence suggesting the measurability of fluctuations in affect over the course of the full interactions.

Paper Nr: 7
Title:

Descriptive Models of Emotion - Learning Useful Abstractions from Physiological Responses during Affective Interactions

Authors:

Rui Henriques and Ana Paiva

Abstract: Supervised recognition of emotions from physiological signals has been widely accomplished to measure affective interactions. Less attention is, however, placed upon learning descriptive models to characterize physiological responses. In this work we delve on why and how to learn discriminative, complete and usable descriptive models based on physiological signals from emotion-evocative stimuli. By satisfying these three properties, we guarantee that the target descriptors can be expressively adopted to understand the physiological behavior underlying multiple emotions. In particular, we explain why classification and unsupervised learning models do not address these properties, and point new directions on how to adapt existing learners to met them based on theoretical and empirical evidence.