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Echo State Network Ensemble for Human Motion Data Temporal Phasing: A Case Study on Tennis Forehands

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Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9950))

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Abstract

Temporal phasing analysis is integral to ubiquitous/“smart” coaching devices and sport science. This study presents a novel approach to autonomous temporal phasing of human motion from captured tennis activity (3D data, 66 time-series). Compared to the optimised Echo State Network (ESN) model achieving 85 % classification accuracy, the ESN ensemble system demonstrates improved classification of 95 % and 100 % accurate phasing state transitions for previously unseen motions without requiring ball impact information. The ESN ensemble model is robust to low-sampling rates (50 Hz) and unbalanced data sets containing incomplete data time-series. The demonstrated achievements are applicable to exergames, augmented coaching and rehabilitation systems advancements by enabling automated qualitative analysis of motion data and generating feedback to aid motor skill and technique improvements.

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Acknowledgements

The author wishes to express his appreciation to developers of Oger Toolbox (http://organic.elis.ugent.be/organic/engine) and Spyder IDE (https://pythonhosted.org/spyder/) utilised in this study. The tennis data was obtained in the Peharec polyclinic for physical therapy and rehabilitation, Pula (Croatia) in collaboration with Petar Bačić (biomechanics lab specialist and professional tennis coach). The author also wishes to express his sincere appreciation to Dr. Stefan Schliebs and Dr. Russel Pears for their valuable comments and insights.

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Authors and Affiliations

  1. School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand

    Boris Bačić

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  1. Boris Bačić
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Correspondence to Boris Bačić .

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Editors and Affiliations

  1. The University of Tokyo , Tokyo, Japan

    Akira Hirose

  2. Kobe University , Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology , Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University , Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences , Beijing, China

    Derong Liu

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Bačić, B. (2016). Echo State Network Ensemble for Human Motion Data Temporal Phasing: A Case Study on Tennis Forehands. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9950. Springer, Cham. https://doi.org/10.1007/978-3-319-46681-1_2

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  • DOI: https://doi.org/10.1007/978-3-319-46681-1_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46680-4

  • Online ISBN: 978-3-319-46681-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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