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International Workshop on Knowledge Representation for Health Care

International Workshop on Transparent, Explainable and Affective AI in Medical Systems

KR4HC 2019, TEAAM 2019: Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems pp 132–147Cite as

A Computational Framework for Interpretable Anomaly Detection and Classification of Multivariate Time Series with Application to Human Gait Data Analysis

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11979))

Abstract

Sensor-based methods for human gait analysis often utilize electromyography capturing rich time-series data. Then, for transparent and explainable analysis interpretable methods are of prime importance. This paper presents analytical approaches in a framework for interpretable anomaly detection and classification of multivariate time series for human gait analysis. We exemplify the application utilizing a real-world medical dataset in the biomechanical orthopedics domain.

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Author information

Authors and Affiliations

  1. Department of Cognitive Science and Artificial Intelligence, Tilburg University, Warandelaan 2, 5037 AB, Tilburg, The Netherlands

    Erica Ramirez & Martin Atzmueller

  2. Orthopedic Surgery, Rush University Medical Center, 1611 W. Harrison Street, Chicago, IL, 60612, USA

    Markus Wimmer

Authors
  1. Erica Ramirez
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  2. Markus Wimmer
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  3. Martin Atzmueller
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Corresponding author

Correspondence to Martin Atzmueller .

Editor information

Editors and Affiliations

  1. Universitat Jaume I, Castellón, Spain

    Mar Marcos

  2. University of Murcia, Murcia, Spain

    Jose M. Juarez

  3. Friedrich-Alexander-University of Erlangen-Nuremberg, Erlangen, Germany

    Richard Lenz

  4. Jagiellonian University and AGH University of Science and Technology, Kraków, Poland

    Grzegorz J. Nalepa

  5. Halmstad University, Halmstad, Sweden

    Slawomir Nowaczyk

  6. University of Haifa, Haifa, Israel

    Mor Peleg

  7. Poznań University of Technology, Poznan, Poland

    Jerzy Stefanowski

  8. University of Maribor, Maribor, Slovenia

    Gregor Stiglic

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Ramirez, E., Wimmer, M., Atzmueller, M. (2019). A Computational Framework for Interpretable Anomaly Detection and Classification of Multivariate Time Series with Application to Human Gait Data Analysis. In: Marcos, M., et al. Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems. KR4HC TEAAM 2019 2019. Lecture Notes in Computer Science(), vol 11979. Springer, Cham. https://doi.org/10.1007/978-3-030-37446-4_11

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  • DOI: https://doi.org/10.1007/978-3-030-37446-4_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37445-7

  • Online ISBN: 978-3-030-37446-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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