Interpreting Psychophysiological States Using Unobtrusive Wearable Sensors in Virtual Reality

One of the main challenges in the study of human be- havior is to quantitatively assess the participants’ affective states by measuring their psychophysiological signals in ecologically valid conditions. The quality of the acquired data, in fact, is often poor due to artifacts generated by natural interactions such as full body movements and gestures. We created a technology to address this problem. We enhanced the eXperience Induction Machine (XIM), an immersive space we built to conduct experiments on human behavior, with unobtrusive wearable sensors that measure electrocardiogram, breathing rate and electrodermal response. We conducted an empirical validation where participants wearing these sensors were free to move in the XIM space while exposed to a series of visual stimuli taken from the International Affective Picture System (IAPS). Our main result consists in the quan- titative estimation of the arousal range of the affective stimuli through the analysis of participants’ psychophysiological states. Taken together, our findings show that the XIM constitutes a novel tool to study human behavior in life-like conditions

From affective computing to empathic adaptive systems: inference and induction of emotion in ecologically-valid contexts to foster novel paradigms of human-computer interaction

Due to the complexity of the natural world, scientific studies on cognitive, behavioral and physiological phenomena are typically conducted under strict laboratory conditions. Nonetheless, recent technological improvements in virtual reality systems and wearable devices are leading to a gradual shift in research towards life-like experimental settings. We present a number of studies focused on the expression and induction of affect in humans that combine quantitative and qualitative data collected through self-reported, behavioral and psychophysiological measures in the eXperience Induction Machine, an immersive space specifically designed to conduct ecologically-valid experiments using virtual and mixed reality. We show that it is possible to empirically investigate emotion in life-like contexts that go beyond standard laboratory setups, while, at the same time, maintaining a high degree of control over the variables measured. Grounded in our results, we introduce BrainX3, an immersive empathic adaptive system for the exploration of complex data from neuroscience. The novelty of our application consists in the coupling of human implicit states with the computational power of machines to explore large datasets and boost the discovery process. Using BrainX3, we demonstrate that human conscious and unconscious intrinsic abilities play a key role in the architectural design of new generation interfaces to tackle scientific challenges such as the “data deluge".Degut a la complexitat del món natural, els estudis científics en fenòmens cognitius, fisiològics i de comportament se solen conduir sota estrictes condicions de laboratori. No obstant això, recents millores tecnològiques en sistemes de realitat virtual i dispositius portables condueixen a un canvi gradual en la recerca cap a escenaris d'experimentació més realistes. Presentem varis estudis enfocats en l'expressió i la inducció d'afecte en humans que combinen dades quantitatives i qualitatives extretes a través de mesures fisiològiques, d'autoavaluació i de comportament en l'eXperience Induction Machine (Màquina d'Inducció d'Experiència), un espai immersiu dissenyat específicament per a conduir experiments validats ecològicament fent servir realitat virtual i mixta. Mostrem que és possible investigar emocions empíricament en contextos realistes que van més enllà de les disposicions estàndard de laboratori, mentre, a la vegada, mantenen un alt grau de control sobre les variables mesurades. Basat en els nostres resultats, introduïm el BrainX3, un sistema adaptatiu, empàtic i immersiu per a l'exploració de dades neurocientífiques complexes. La novetat de la nostra aplicació consisteix en la connexió entre estats implícits de l'ésser humà i el poder computacional de les màquines per a explorar grans bases de dades i potenciar el procés de descobriment. Fent servir BrainX3, demostrem que les habilitats intrínseques conscients i inconscients de l'humà juguen un paper primordial en el disseny de l'arquitectura d'interfícies de nova generació per a abordar reptes científics com el devessall de dades

The Affective Slider: A Digital Self-Assessment Scale for the Measurement of Human Emotions.

Self-assessment methods are broadly employed in emotion research for the collection of subjective affective ratings. The Self-Assessment Manikin (SAM), a pictorial scale developed in the eighties for the measurement of pleasure, arousal, and dominance, is still among the most popular self-reporting tools, despite having been conceived upon design principles which are today obsolete. By leveraging on state-of-the-art user interfaces and metacommunicative pictorial representations, we developed the Affective Slider (AS), a digital self-reporting tool composed of two slider controls for the quick assessment of pleasure and arousal. To empirically validate the AS, we conducted a systematic comparison between AS and SAM in a task involving the emotional assessment of a series of images taken from the International Affective Picture System (IAPS), a database composed of pictures representing a wide range of semantic categories often used as a benchmark in psychological studies. Our results show that the AS is equivalent to SAM in the self-assessment of pleasure and arousal, with two added advantages: the AS does not require written instructions and it can be easily reproduced in latest-generation digital devices, including smartphones and tablets. Moreover, we compared new and normative IAPS ratings and found a general drop in reported arousal of pictorial stimuli. Not only do our results demonstrate that legacy scales for the self-report of affect can be replaced with new measurement tools developed in accordance to modern design principles, but also that standardized sets of stimuli which are widely adopted in research on human emotion are not as effective as they were in the past due to a general desensitization towards highly arousing content

The Self-Assessment Manikin (SAM), adapted with permission from Bradley and Lang 1994 [6].

<p>SAM is a pictorial tool designed in the eighties that measures pleasure (top), arousal (middle) and dominance (bottom) on a discrete scale. It is available in two main versions: paper-and-pencil (5-, 7-, 9-points) and computer program (20-points). Participants can rate their affective state by placing an X over or between any figure.</p

Towards guidelines on educational podcasting quality: problems arising from a real world experience

This paper presents an experience of educational podcasting set up at the University of Bergamo (Italy), and derives from that experience some remarks upon the quality of podcasting services, in order to promote the definition of guidelines on podcasting quality. We discuss three main attributes of a podcasting environment: quality of the production environment (recording and editing), quality of the product (content and communication style), quality of the distribution environment (paratext and management)

Screenshot of the web-based questionnaire showing a single experimental trial using the AS.

<p>The picture (in this example just a placeholder and not part of the IAPS collection) is randomly displayed either on the left or on the right side of the screen. Similarly, the order of the pleasure and arousal dimensions is randomized.</p

Scatterplots representing the linear correlation between AS and SAM pleasure (r_s = .852) and arousal (r_s = .860).

<p>The red line indicates the best fit.</p

The “Affective Slider” (AS) is a digital self-reporting tool composed of two sliders that measure arousal (top) and pleasure (bottom) on a continuous scale.

<p>The AS does not require written instructions and it is intentionally displayed using a neutral chromatic palette to avoid bias in ratings due to the emotional connotation of colors. See text for more details.</p