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Understanding social signals from nonverbal behaviors in a mobile setting

Jia, Xin (2017) Understanding social signals from nonverbal behaviors in a mobile setting.

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Abstract:Nonverbal behaviors are natural yet critical channels in understanding social signals. The automation of the apprehension of such signals has been an increasingly popular topic in recent decades, due to the development of the recording hardware as well as the machine analysis capabilities. In this study, predictive models for measuring the emotions, attitudes and personalities of individuals from nonverbal behaviors were established. The realization of the model involved the construction of a multimodal and mobile recording framework of behaviors, the collection of individuals’ emotions, attitudes and personalities as ground truths, and the application of various machine learning algorithms which find and interpret the patterns in the data. A user study was designed in order to obtain the necessary visual, audio and spatial data. 20 participants were recruited and requested to have dyadic conversations with pedestrians on the street. The conversations were recorded and then processed in order to extract the following features: facial expression, gaze location, interpersonal distance, speech data and so forth. Furthermore, the participants reported their experiences after each conversation, including the perceived friendliness of the pedestrian, the levels of frustration after the conversation, as well as their emotion states in the arousal-valence mode. Finally, the participants completed a set of psychological questionnaires regarding their personality and racial prejudice level at the end of the whole experiment.
Item Type:Essay (Master)
Clients:
Max Planck Institute for Informatics, Saarbruecken, Germany
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Human Media Interaction MSc (60030)
Link to this item:http://purl.utwente.nl/essays/73526
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