Researcher:
Madan, Çığıl Ece

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Master Student

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Çığıl Ece

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Madan

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Madan, Çığıl Ece

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    Recognition of haptic interaction patterns in dyadic joint object manipulation
    (IEEE Computer Society, 2015) KucukYılmaz, Ayse; N/A; Department of Computer Engineering; Department of Mechanical Engineering; Madan, Çığıl Ece; Sezgin, Tevfik Metin; Başdoğan, Çağatay; Master Student; Faculty Member; Faculty Member; Department of Computer Engineering; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; College of Engineering; College of Engineering; N/A; 18632; 125489
    The development of robots that can physically cooperate with humans has attained interest in the last decades. Obviously, this effort requires a deep understanding of the intrinsic properties of interaction. Up to now, many researchers have focused on inferring human intents in terms of intermediate or terminal goals in physical tasks. On the other hand, working side by side with people, an autonomous robot additionally needs to come up with in-depth information about underlying haptic interaction patterns that are typically encountered during human-human cooperation. However, to our knowledge, no study has yet focused on characterizing such detailed information. In this sense, this work is pioneering as an effort to gain deeper understanding of interaction patterns involving two or more humans in a physical task. We present a labeled human-human-interaction dataset, which captures the interaction of two humans, who collaboratively transport an object in an haptics-enabled virtual environment. In the light of information gained by studying this dataset, we propose that the actions of cooperating partners can be examined under three interaction types: In any cooperative task, the interacting humans either 1) work in harmony, 2) cope with conflicts, or 3) remain passive during interaction. In line with this conception, we present a taxonomy of human interaction patterns; then propose five different feature sets, comprising force-, velocity-and power-related information, for the classification of these patterns. Our evaluation shows that using a multi-class support vector machine (SVM) classifier, we can accomplish a correct classification rate of 86 percent for the identification of interaction patterns, an accuracy obtained by fusing a selected set of most informative features by Minimum Redundancy Maximum Relevance (mRMR) feature selection method.