Alle Publikationen
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2017
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Burgoon, Judee K.; Magnenat-Thalmann, Nadia; Pantic, Maja (Hg.) (2017): Social signal processing. Cambridge: Cambridge University Press
Abstract: Social Signal Processing is the first book to cover all aspects of the modeling, automated detection, analysis, and synthesis of nonverbal behavior in human-human and human-machine interactions. Authoritative surveys address conceptual foundations, machine analysis and synthesis of social signal processing, and applications. Foundational topics include affect perception and interpersonal coordination in communication; later chapters cover technologies for automatic detection and understanding such as computational paralinguistics and facial expression analysis and for the generation of artificial social signals such as social robots and artificial agents. The final section covers a broad spectrum of applications based on social signal processing in healthcare, deception detection, and digital cities, including detection of developmental diseases and analysis of small groups. Each chapter offers a basic introduction to its topic, accessible to students and other newcomers, and then outlines challenges and future perspectives for the benefit of experienced researchers and practitioners in the field.
Keywords: Computer vision, Computerwissenschaft, Disziplin, doppelter Treffer, Favoriten, Human behaviour analysis', Interaktion, Mensch-Technik-Relationen (MTR), Modelle/Theorien, Realtechnik, Sammelband, Signal, Signale, Signaling social preferences/ Social signal processing, Social interactions, social signal processing, Social Signal Processing (SSP), social signalling, Social signals, Soziale Angemessenheit, soziale Kognition, Soziosensitive Systeme, speech processing, Technik -
(2017) : Social Signal Processing in Social Robotics In: Vinciarelli, Alessandro; Burgoon, Judee K.; Pantic, Maja; Magnenat-Thalmann, Nadia (Hg.): Social signal processing: Cambridge: Cambridge University Press, S. 317-328
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(2017) : Introduction: Social Signal Processing In: Vinciarelli, Alessandro; Burgoon, Judee K.; Pantic, Maja; Magnenat-Thalmann, Nadia (Hg.): Social signal processing: Cambridge: Cambridge University Press, S. 1-8
2015
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(2015) : Perception of Affective Body Movements in HRI across Age Groups: Comparison between Results from Denmark and Japan: 2015 International Conference on Culture and Computing (Culture Computing): Kyoto, Japan: IEEE, S. 25-32
DOI: https://doi.org/10.1109/Culture.and.Computing.2015.14 Abstract: Social robots are envisioned to move into unrestricted environments where they will be interacting with naive users (in terms of their experience as robot operators). Thus, these robots are also envisioned to exploit interaction channels that are natural to humans like speech, gestures, or body movements. A specificity of these interaction channels is that humans do not only convey task-related information but also more subtle information like e.g. Emotions or personal stance through these channels. Thus, to be successful and not accidentally jeopardizing an interaction, robots need to be able to understand these implicit connotations of the signals (often called social signal processing) in order to generate appropriate signals in a given interaction context. One main application area that is envisioned for social robots is related to elder care, but little is known on how seniors will perceive robots and the signals they produce. In this paper we focus on affective connotations of body movements and investigate how the perception of body movements of robots is related to age. Inspired by a study from Japan, we introduce culture as a variable in the experiment and discuss the difficulties of cross-cultural comparisons. The results show that there are certain age-related differences in the perception of affective body movements, but not as strong as in the original study. A follow up experiment puts the affective body movements into context and shows that recognition rates deteriorate for older participants.
Keywords: affective body movement perception, affective body movements, age groups, age-related differences, Angemessen(heit) (von Technik), assisted living, Context, cross-cultural analysis, Cultural differences, culturally aware technology, Denmark, elder care, emotion information, Face, HRI, Human Factors, Human robot interaction, Humanoid Robots, human-robot interaction, ieee xplore, implicit connotation, interaction channels, Japan, Legged locomotion, Observers, personal stance information, robot operators, Robots, service robot, social robots, social signal processing, task-related information, TV, unrestricted environments, Videos 2013
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(2013): Computationally Modeling Interpersonal Trust. In: Frontiers in psychology 4. DOI: 10.3389/fpsyg.2013.00893
DOI: https://doi.org/10.3389/fpsyg.2013.00893 Abstract: We present a computational model capable of predicting—above human accuracy—the degree of trust a person has toward their novel partner by observing the trust-related nonverbal cues expressed in their social interaction. We summarize our prior work, in which we identify nonverbal cues that signal untrustworthy behavior and also demonstrate the human mind’s readiness to interpret those cues to assess the trustworthiness of a social robot. We demonstrate that domain knowledge gained from our prior work using human-subjects experiments, when incorporated into the feature engineering process, permits a computational model to outperform both human predictions and a baseline model built in naivete' of this domain knowledge. We then present the construction of hidden Markov models to incorporate temporal relationships among the trust-related nonverbal cues. By interpreting the resulting learned structure, we observe that models built to emulate different levels of trust exhibit different sequences of nonverbal cues. From this observation, we derived sequence-based temporal features that further improve the accuracy of our computational model. Our multi-step research process presented in this paper combines the strength of experimental manipulation and machine learning to not only design a computational trust model but also to further our understanding of the dynamics of interpersonal trust.
2012
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(2012): Bridging the Gap between Social Animal and Unsocial Machine: A Survey of Social Signal Processing. In: IEEE Transactions on Affective Computing 3 (1), S. 69-87. DOI: 10.1109/T-AFFC.2011.27
2008
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(2008): Proceedings of the 16th ACM international conference on Multimedia. New York, NY: ACM. Online verfügbar unter http://dl.acm.org/citation.cfm?id=1459359
2007
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(2007): Social Signal Processing [Exploratory DSP]. In: IEEE Signal Processing Magazine 24 (4), S. 108-111. DOI: 10.1109/MSP.2007.4286569
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