Data-driven knowledge discovery is becoming a new trend in various scientific fields. In light of this, the goal of the present paper is to introduce a novel framework to study one interesting topic in cognitive and behavioral studies - multimodal communication between human-human and human-robot interaction. We present an overall solution from data capture, through data coding and validation, to data analysis and visualization. In data collection, we have developed a multimodal sensing system to gather fine-grained video, audio and human body movement data. In data analysis, we propose a hybrid solution based on visual data mining and information-theoretic measures. We suggest that this data-driven paradigm will lead not only to breakthroughs in understanding multimodal communication, but will also serve as a successful case study to demonstrate the promise of data-intensive discovery which can be applied in various research topics in cognitive and behavioral studies. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Yu, C., Smith, T. G., Hidaka, S., Scheutz, M., & Smith, L. B. (2010). A data-driven paradigm to understand multimodal communication in human-human and human-robot interaction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6065 LNCS, pp. 232–244). https://doi.org/10.1007/978-3-642-13062-5_22
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