Abstract
Touch is an important form of social interaction. In Human Robot Interaction (HRI), touch can provide additional information to other modalities, such as audio, visual. In this paper, an ensemble classifier based on three-way decisions is proposed to recognize touch gestures. Firstly, features are extracted from six perspectives and four classifiers are constructed on different scales with different preprocessing methods. Then an ensemble classifier is used to combine the four classifiers to classify touch gestures. Our method is tested on the public Corpus of Social Touch (CoST) dataset. The experiment results not only verify the validity of our method but also show a better performance of our ensemble classifier.
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Acknowledgments
The work is supported by the Key Research and Development Program of Chongqing (cstc2017zdcy-zdyfx0091) and the Key Research and Development Program on AI of Chongqing (cstc2017rgzn-zdyfx0022) and the National Nature Science Foundation of China (61572091).
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Zhang, G., Liu, Q., Shi, Y., Meng, H. (2018). An Ensemble Classifier Based on Three-Way Decisions for Social Touch Gesture Recognition. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10942. Springer, Cham. https://doi.org/10.1007/978-3-319-93818-9_35
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DOI: https://doi.org/10.1007/978-3-319-93818-9_35
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