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2013
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(2013): Automated Proxemic Feature Extraction and Behavior Recognition. Applications in Human-Robot Interaction. In: International Journal of Social Robotics 5 (3), S. 367-378. DOI: 10.1007/s12369-013-0189-8
Abstract: Abstract In this work, we discuss a set of feature representations for analyzing human spatial behavior (proxemics) motivated by metrics used in the social sciences. Specifically, we consider individual, physical, and psychophysical factors that contribute to social spacing. We demonstrate the feasibility of autonomous real-time annotation of these proxemic features during a social interaction between two people and a humanoid robot in the presence of a visual obstruction (a physical barrier). We then use two different feature representations-physical and psychophysical-to train Hidden Markov Models (HMMs) to recognize spatiotemporal behaviors that signify transitions into (initiation) and out of (termination) a social interaction. We demonstrate that the HMMs trained on psychophysical features, which encode the sensory experience of each interacting agent, outperform those trained on physical features, which only encode spatial relationships. These results suggest a more powerful representation of proxemic behavior with particular implications in autonomous socially interactive and socially assistive robotics.
Keywords: Assistenztechnik, doppelter Treffer, Favoriten, Ingenieurswissenschaft, Interaktion, Interaktionsaustritt, Interaktionseintritt, Mensch-Technik-Relationen (MTR), Observablen/Kriterien für sozial angemessenes Verhalten und dessen Bewertung, Proxemik, Realtechnik, Soziale Robotik, Sozialer Raum / Kultureller Kontext, sozialintervenierende Agenten, Soziosensitive Systeme, Status / biologische Marker, Technik, termination of social interaction
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