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Detecting slipping-like perturbations by using adaptive oscillators.

Peppino Tropea1, Nicola Vitiello, Dario Martelli

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

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This study presents a new algorithm for early detection of slipping-like perturbations using leg joint angles. The method accurately identifies balance challenges during locomotion with a low false alarm rate.

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Area of Science:

  • Biomechanics
  • Robotics
  • Control Systems

Background:

  • Detecting unexpected perturbations during locomotion is crucial for maintaining dynamic balance.
  • Existing methods may require extensive training or be computationally intensive.

Purpose of the Study:

  • To develop and validate a novel algorithm for early detection of slipping-like perturbations.
  • To assess the algorithm's performance in terms of detection time and accuracy.

Main Methods:

  • A novel algorithm comparing actual leg joint angles with predicted angles from adaptive oscillators was developed.
  • Leg joint angles of twelve healthy participants were recorded during four different perturbations.
  • The algorithm estimated joint angles across the adaptive oscillator dynamics.

Main Results:

  • The algorithm successfully detected postural transitions induced by slipping-like perturbations.
  • The mean detection time ranged from 150 to 250 milliseconds.
  • A low false alarm rate (less than 10%) was achieved.

Conclusions:

  • The proposed algorithm provides an efficient and simple method for early perturbation detection.
  • The approach is not computationally heavy, not subject-dependent, and requires no falling-based training.
  • The algorithm's reliance on leg joint angles makes it suitable for integration into lower-limb orthoses and prostheses.