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Multi-complexity Ensemble Measures for Gait Time Series Analysis: Application to Diagnostics, Monitoring and Biometrics

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Signal and Image Analysis for Biomedical and Life Sciences

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 823))

Abstract

Previously, we have proposed to use complementary complexity measures discovered by boosting-like ensemble learning for the enhancement of quantitative indicators dealing with necessarily short physiological time series. We have confirmed robustness of such multi-complexity measures for heart rate variability analysis with the emphasis on detection of emerging and intermittent cardiac abnormalities. Recently, we presented preliminary results suggesting that such ensemble-based approach could be also effective in discovering universal meta-indicators for early detection and convenient monitoring of neurological abnormalities using gait time series. Here, we argue and demonstrate that these multi-complexity ensemble measures for gait time series analysis could have significantly wider application scope ranging from diagnostics and early detection of physiological regime change to gait-based biometrics applications.

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Author information

Authors and Affiliations

  1. Department of Physics, West Virginia University, Morgantown, WV, 26506, USA

    Valeriy Gavrishchaka

  2. Department of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Leninskie Gory, 119991, Moscow, Russia

    Olga Senyukova

  3. Department of Pathology, University of Michigan, Ann Arbor, MI, 48109, USA

    Kristina Davis

Authors
  1. Valeriy Gavrishchaka
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  2. Olga Senyukova
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  3. Kristina Davis
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Corresponding author

Correspondence to Valeriy Gavrishchaka .

Editor information

Editors and Affiliations

  1. Digital Productivity Flagship, CSIRO, Sydney, New South Wales, Australia

    Changming Sun

  2. Digital Productivity Flagship, CSIRO, Sydney, New South Wales, Australia

    Tomasz Bednarz

  3. The University of Aizu, Fukushima, Japan

    Tuan D. Pham

  4. Digital Productivity Flagship, CSIRO, Sydney, New South Wales, Australia

    Pascal Vallotton

  5. Digital Productivity Flagship, CSIRO, Sydney, New South Wales, Australia

    Dadong Wang

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Gavrishchaka, V., Senyukova, O., Davis, K. (2015). Multi-complexity Ensemble Measures for Gait Time Series Analysis: Application to Diagnostics, Monitoring and Biometrics. In: Sun, C., Bednarz, T., Pham, T., Vallotton, P., Wang, D. (eds) Signal and Image Analysis for Biomedical and Life Sciences. Advances in Experimental Medicine and Biology, vol 823. Springer, Cham. https://doi.org/10.1007/978-3-319-10984-8_6

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