A neural network based approach to social touch classification

Siewart van Wingerden, Tobias J. Uebbing, Merel Madeleine Jung, Mannes Poel

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    8 Citations (Scopus)
    86 Downloads (Pure)

    Abstract

    Touch is an important interaction modality in social interaction, for instance touch can communicate emotions and can intensify emotions communicated by other modalities. In this paper we explore the use of Neural Networks for the classification of touch. The exploration and assessment of Neural Networks (NNs) is based on the Corpus of Social Touch established by Jung et al. This corpus was split in a train set (65%) and test set (35%), the train set was used to find the optimal parameters for the NN and for training the final model. Also different feature sets were investigated; the basic feature set included in the corpus, energy-histogram and dynamical features. Using all features led to the best performance of 64% on the test set, using a NN consisting of one hidden layer with 46 neurones. The confusion matrix showed the expected high confusion between pat-tap and grab-squeeze. A leave-one-subject-out approach lead to a performance of 54%, which is comparable with the results of Jung et al.
    Original languageUndefined
    Title of host publicationProceedings of the 2nd International Workshop on Emotion representations and modelling in Human-Computer Interaction systems, ERM4HCI 2014
    Place of PublicationNew York
    PublisherAssociation for Computing Machinery (ACM)
    Pages7-12
    Number of pages6
    ISBN (Print)978-1-4503-0124-4
    DOIs
    Publication statusPublished - Nov 2014

    Publication series

    NameERM4HCI '14
    PublisherACM

    Keywords

    • EWI-25281
    • Social Touch
    • METIS-309649
    • IR-93289
    • Neural Networks
    • Touch gesture recognition

    Cite this

    van Wingerden, S., Uebbing, T. J., Jung, M. M., & Poel, M. (2014). A neural network based approach to social touch classification. In Proceedings of the 2nd International Workshop on Emotion representations and modelling in Human-Computer Interaction systems, ERM4HCI 2014 (pp. 7-12). (ERM4HCI '14). New York: Association for Computing Machinery (ACM). https://doi.org/10.1145/2668056.2668060