Learning to judge like a human: Convolutional networks for classification of ski jumping errors

Heike Brock, Yuji Ohgi, James Lee

    Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedings

    Abstract

    Advanced machine learning technologies are seldom applied to wearable motion sensor data obtained from sport movements. In this work, we therefore investigated neural networks for motion performance evaluation utilizing a set of inertial sensor-based ski jump measurements. A multidimensional convolutional network model that related the motion data under aspects of time, placement and sensor type was implemented. Additionally, its applicability as a measure for automatic motion style judging was evaluated. Results indicate that one multi-dimensional convolutional layer is sufficient to recognize relevant performance error representations. Furthermore, comparisons against a Support Vector Machine and a Hidden Markov Model show that the new model outperforms feature-based methods under noisy and biased data environments. Architectures such as the proposed evaluation system can hence become essential for automatic performance analysis and style judging systems in future.

    Original languageEnglish
    Title of host publicationISWC 2017 - Proceedings of the 2017 ACM International Symposium on Wearable Computers
    PublisherAssociation for Computing Machinery (ACM)
    Pages106-113
    Number of pages8
    VolumePart F130534
    ISBN (Electronic)9781450351881
    DOIs
    Publication statusPublished - 11 Sep 2017
    Event29th ACM International Symposium on Wearable Computers, ISWC 2017 - Maui, United States
    Duration: 11 Sep 201715 Sep 2017

    Conference

    Conference29th ACM International Symposium on Wearable Computers, ISWC 2017
    CountryUnited States
    CityMaui
    Period11/09/1715/09/17

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  • Cite this

    Brock, H., Ohgi, Y., & Lee, J. (2017). Learning to judge like a human: Convolutional networks for classification of ski jumping errors. In ISWC 2017 - Proceedings of the 2017 ACM International Symposium on Wearable Computers (Vol. Part F130534, pp. 106-113). Association for Computing Machinery (ACM). https://doi.org/10.1145/3123021.3123038