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Comparison of algorithms and classifiers for stride detection using wearables

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Abstract

Sensor-based systems for diagnosis or therapy support of motor dysfunctions need methodologies of automatically stride detection from movement sequences. In this proposal, we developed a stride detection system for daily life use. We compared mostly used algorithms min–max patterns, dynamic time warping, convolutional neural networks (CNN), and automatic framing using two data sets of 32 healthy and 28 Parkinson’s disease (PD) persons. We developed an insole with force and IMU sensors to record the gait data. The PD patients carried out the standardized time up and go test, and the healthy persons a daily life activities test (walking, sitting, standing, ascending and descending stairs). As an automatically stride detection process for daily life use, we propose a first stride detection using automatic framing, and after normalization and resampling data a CNN is used. A F1-score of 0.938 (recall 0.968, precision 0.910) for time up and go test and of 0.944 (recall 0.992, precision 0.901) for daily life activities test were obtained for CNN. Compared to the other detection methods, up to 6% F-measure improvement was shown.

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Authors and Affiliations

  1. Brandenburg University of Technology Cottbus-Senftenberg, Institute of Medical Technology, Universitätsplatz 1, 01968, Senftenberg, Germany

    Tobias Steinmetzer, Ingrid Bönninger, Markus Reckhardt & Fritjof Reinhardt

  2. University of Las Palmas de Gran Canaria, Empresa, Internet y Tecnologías de las Comunicaciones, Las Palmas de Gran Canaria, Spain

    Tobias Steinmetzer

  3. University of Las Palmas de Gran Canaria, Signals and Communication Department, IDeTIC, Las Palmas de Gran Canaria, Spain

    Carlos M. Travieso

  4. Niederlausitz Clinic, Center of Neurology and Pain Management, Senftenberg, Germany

    Markus Reckhardt, Fritjof Reinhardt & Dorela Erk

Authors
  1. Tobias Steinmetzer
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  2. Ingrid Bönninger
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  4. Fritjof Reinhardt
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  6. Carlos M. Travieso
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Correspondence to Tobias Steinmetzer.

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Steinmetzer, T., Bönninger, I., Reckhardt, M. et al. Comparison of algorithms and classifiers for stride detection using wearables. Neural Comput & Applic 32, 17857–17868 (2020). https://doi.org/10.1007/s00521-019-04384-6

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  • DOI: https://doi.org/10.1007/s00521-019-04384-6

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