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Trustworthiness and Confidence of Gait Phase Predictions in Changing Environments Using Interpretable Classifier Models

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Neural Information Processing (ICONIP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13624))

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Abstract

The recognition of the different phases of human gait is valuable in areas such as rehabilitation and sports. Machine Learning models have been increasingly used for such recognition tasks. However, such models are usually trained on data obtained from participants in strictly controlled environments which—needless to say—might vary quite significantly from the environment in which the models are subsequently employed. Therefore, it is advisable to analyze the confidence of the model’s predictions. To this end, we present an interpretable classifier for gait phase detection. Together with classification reliability estimation tools, classification predictions can be rejected in low confidence scenarios. Our classifier is based on a robust and distance-based Learning Vector Quantization classifier. Finally, we present our approach using a real-world application in gait phase detection, which consists of one learning scenario and two different prediction scenarios.

M.K is funded by the European Social Fund (ESF), ESF-SAB 100381749.

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Notes

  1. 1.

    alaska/Dynamicus is a module for efficient, and comfortable generation and use of maker-based-system models of the human body provided by the ICM - Institute Chemnitzer Maschinen- und Anlagenbau e.V, Chemnitz, Germany.

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Authors and Affiliations

  1. ICM - Institute Chemnitzer Maschinen - und Anlagenbau e.v., Otto-Schmerbach-Street 19, 09117, Chemnitz, Germany

    Danny Möbius

  2. University of Applied Sciences Mittweida, Technikumpatz 17, 09648, Mittweida, Germany

    Jensun Ravichandran, Marika Kaden & Thomas Villmann

Authors
  1. Danny Möbius
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  2. Jensun Ravichandran
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  3. Marika Kaden
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  4. Thomas Villmann
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Corresponding author

Correspondence to Thomas Villmann .

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Editors and Affiliations

  1. Indian Institute of Technology Indore, Indore, India

    Mohammad Tanveer

  2. Indian Institute of Information Technology - Allahabad, Prayagraj, India

    Sonali Agarwal

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

  4. Indian Institute of Technology Patna, Patna, India

    Asif Ekbal

  5. University of Innsbruck, Innsbruck, Austria

    Adam Jatowt

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Möbius, D., Ravichandran, J., Kaden, M., Villmann, T. (2023). Trustworthiness and Confidence of Gait Phase Predictions in Changing Environments Using Interpretable Classifier Models. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13624. Springer, Cham. https://doi.org/10.1007/978-3-031-30108-7_32

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  • DOI: https://doi.org/10.1007/978-3-031-30108-7_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30107-0

  • Online ISBN: 978-3-031-30108-7

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