Control of a hybrid upper-limb orthosis device based on a data-driven artificial neural network classifier of electromyography signals

Francisco Medina, Kenny Perez, David Cruz-Ortiz, Mariana Ballesteros, Isaac Chairez

Research output: Contribution to journalArticlepeer-review

Abstract

This study presents the design of a functional electrical stimulation system (FES) for upper limb, including the construction and evaluation of an integral self-carrying rehabilitation device. The proposed device incorporated a hybrid system that performs the active mobilization control for the articulations or the assisted movement for the upper limb using electrical stimulation, as well as an on-line characterization of the electromyographic (EMG) signals captured in the trapezius and deltoid muscles. The orthosis was manufactured using a three-dimensional printer. The constructed device was electronically instrumented as a fully actuated robot, and it was controlled in a decentralized form by a set of state feedback (proportional-derivative) algorithms. This study proposed an interpolation method based on sigmoidal functions to solve the trajectory tracking for each actuated articulation. These algorithms used the estimated time-derivative of the tracking error provided by several explicit discretized super-twisting differentiators. The EMG signals were classified by a static multilayered artificial neural network trained with the Levenberg-Marquardt method, defining the movement intention triggered by the user. The device was tested in simulation software including the integration of several evaluation scenarios depending on the classified EMG signal and the tracking trajectory performance developed by the suggested state feedback controllers. The constructed orthosis was successfully evaluated with four volunteers showing the expected performance according to the proposed evaluation scenarios.

Original languageEnglish
Article number102624
JournalBiomedical Signal Processing and Control
Volume68
DOIs
StatePublished - Jul 2021

Keywords

  • Artificial neural networks
  • Electromiography signal
  • Functional electrical stimulation
  • Robust PID controller
  • Upper limb orthosis

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