02338nas a2200229   4500008004100000245012400041210006900165490000600234520155800240100001601798700001401814700001701828700001401845700001401859700001501873700001701888700002101905700001401926700001701940700001701957856013401974       2014                        eng d00aInference of Human Affective States from Psychophysiological Measurements Extracted under Ecologically Valid Conditions0 aInference of Human Affective States from Psychophysiological Mea0 v83 a<p>Compared to standard laboratory protocols, the measurement of psychophysiological signals in real world experiments poses technical and methodological challenges due to external factors that cannot be directly controlled. To address this problem, we propose a hybrid approach based on an immersive and human accessible space called the eXperience Induction Machine (XIM), that incorporates the advantages of a laboratory within a life-like setting. The XIM integrates unobtrusive wearable sensors for the acquisition of psychophysiological signals suitable for ambulatory emotion research. In this paper, we present results from two different studies conducted to validate the XIM as a general-purpose sensing infrastructure for the study of human affective states under ecologically valid conditions. In the first investigation, we recorded and classified signals from subjects exposed to pictorial stimuli corresponding to a range of arousal levels, while they were free to walk and gesticulate. In the second study, we designed an experiment that follows the classical conditioning paradigm, a well-known procedure in the behavioral sciences, with the additional feature that participants were free to move in the physical space, as opposed to similar studies measuring physiological signals in constrained laboratory settings. Our results indicate that, by using our sensing infrastructure, it is indeed possible to infer human event-elicited affective states through measurements of psychophysiological signals under ecological conditions.</p>
1 aBetella, A.1 aZucca, R.1 aCetnarski, A1 aGreco, A.1 aLanata, A1 aMazzei, D.1 aTognetti, A.1 aArsiwalla, X. D.1 aOmedas, P1 aDe Rossi, D.1 aVerschure, P  uhttps://www.centropiaggio.unipi.it/publications/inference-human-affective-states-psychophysiological-measurements-extracted-under02009nas a2200265   4500008003900000245009800039210006900137520115700206100001601363700001601379700001401395700002101409700001401430700001401444700001501458700001701473700001401490700001801504700001501522700002101537700001501558700001701573700001801590856013501608       2014                          d00aInterpreting Psychophysiological States Using Unobtrusive Wearable Sensors in Virtual Reality0 aInterpreting Psychophysiological States Using Unobtrusive Wearab3 a<p>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.</p>
1 aBetella, A.1 aPacheco, D.1 aZucca, R.1 aArsiwalla, X. D.1 aOmedas, P1 aLanata, A1 aMazzei, D.1 aTognetti, A.1 aGreco, A.1 aCarbonaro, N.1 aWagner, J.1 aLingenfelser, F.1 aAndrè, E.1 aDe Rossi, D.1 aVerschure, P.  uhttps://www.centropiaggio.unipi.it/publications/interpreting-psychophysiological-states-using-unobtrusive-wearable-sensors-virtual