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Detecting Slipping-Like Perturbations by Using Adaptive Oscillators

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Abstract

This study introduces a novel algorithm to detect unexpected slipping-like perturbations based on the comparison between actual leg joint angles and those predicted by a pool of adaptive oscillators. The approach grounds on the hypothesis that during postural transitions, the difference between these datasets diverges and can early signal that the dynamic balance is challenged. To test this hypothesis, leg joint angles of twelve healthy young participants were recorded while undergoing four different perturbations delivered during steady locomotion. Joint angles were estimated after spanning the whole domain of the adaptive oscillator dynamics. Results confirmed that the implemented strategy allows to early detect a postural transition induced by a slipping-like perturbation: the best performance is represented by a mean detection time ranging between 150 and 250 ms and a low rate (lower than 10%) of false alarms. On the whole, the proposed approach is efficient even if it is based on a quite simple threshold-based algorithm. Moreover, it does not need any falling-based training before being implemented, is not computationally heavy, and is not subject dependent. Finally, since it is based on leg joint angles, it appears well suited to be implemented in lower-limb orthoses/prostheses already equipped with joint position sensors.

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Acknowledgments

This work was supported by the European Union within the CYBERLEGs (The CYBERnetic LowEr-Limb CoGnitive Ortho-prosthesis, ICT 287894) and the I-DONT-FALL (Integrated prevention and Detection sOlutioNs Tailored to the population and Risk Factors associated with FALLs, CIP-ICT-PSP-2011-5-297225) projects.

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Authors and Affiliations

  1. The BioRobotics Institute, Scuola Superiore Sant’Anna, viale Rinaldo Piaggio 34, 56025, Pontedera, Pisa, Italy

    Peppino Tropea, Nicola Vitiello, Dario Martelli, Federica Aprigliano, Silvestro Micera & Vito Monaco

  2. Don Carlo Gnocchi Foundation, via di Scandicci, 265, 50143, Florence, Italy

    Nicola Vitiello

  3. Translational Neural Engineering Laboratory, Center for Neuroprosthetics and Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, 1015, Switzerland

    Silvestro Micera

Authors
  1. Peppino Tropea
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  2. Nicola Vitiello
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  3. Dario Martelli
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  4. Federica Aprigliano
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  5. Silvestro Micera
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  6. Vito Monaco
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Corresponding author

Correspondence to Vito Monaco.

Additional information

Associate Editor Amit Gefen oversaw the review of this article.

Peppino Tropea and Nicola Vitiello equally contributed to this study.

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Tropea, P., Vitiello, N., Martelli, D. et al. Detecting Slipping-Like Perturbations by Using Adaptive Oscillators. Ann Biomed Eng 43, 416–426 (2015). https://doi.org/10.1007/s10439-014-1175-5

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  • DOI: https://doi.org/10.1007/s10439-014-1175-5

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