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Markov jump linear systems-based position estimation for lower limb exoskeletons.

Samuel L Nogueira1, Adriano A G Siqueira2, Roberto S Inoue3

  • 1Department of Mechanical Engineering, University of São Paulo, São Carlos, SP 13566-590, Brazil. samlourenco@gmail.com.

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Summary

This study introduces a Markov Jump Linear Systems approach for robotic rehabilitation exoskeletons. It improves angular position estimation by collectively modeling all sensors, enhancing accuracy for stroke and spinal cord injury patients.

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Area of Science:

  • Robotics
  • Biomedical Engineering
  • Control Systems

Background:

  • Robotic exoskeletons aid stroke and spinal cord injury patients in walking rehabilitation.
  • Standard Kalman filters (KF) estimate link positions using inertial measurement units (IMUs) but lack collective sensor integration.
  • Multi-body systems like exoskeletons present challenges for individual sensor-based estimation.

Purpose of the Study:

  • To enhance angular position estimation in impedance-controlled exoskeletons for rehabilitation.
  • To propose a novel collective sensor modeling approach using Markovian estimation.
  • To improve the accuracy and robustness of position estimation in lower limb exoskeletons.

Main Methods:

  • Application of Markov Jump Linear Systems (MJLS) for filtering in robotic rehabilitation.
  • Collective modeling of multiple inertial sensors (IMUs) and encoders attached to an exoskeleton.
  • Comparative analysis of the proposed Markovian estimation system against standard Kalman filters.

Main Results:

  • The proposed Markovian estimation system demonstrated improved accuracy in angular position estimation.
  • Simulation results validated the effectiveness of collective sensor modeling for lower limb exoskeletons.
  • The system showed robustness against a wide range of parametric uncertainties.

Conclusions:

  • Collective modeling of all sensors in an exoskeleton using MJLS-based filtering significantly improves position estimation.
  • This approach offers a more comprehensive and accurate solution for robotic rehabilitation systems.
  • The method provides enhanced performance compared to standard estimation techniques, especially under uncertainty.