Control of 1-DOF exoskeleton based on neural network regression analysis and wavelet transform of MES

Rafael Puerta, Andres López, Lizeth Roldan, Diego Patino

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Improvement of human locomotor performance through external devices such as exoskeletons is a field of active research. This paper presents the design and implementation of an exoskeleton of one degree of freedom (DOF) to assist the flexion and extension of the upper limb. The exoskeleton is controlled by signals from force sensors and myoelectric signals (MES), achieving a reduction of the muscle activity of the user. The MES are captured from the triceps and biceps muscle groups. Subsequent digital signal processing comprises: for feature extraction of signals the time-frequency Wavelet transform is performed, and its following regression analysis is done by an artificial neural network (ANN). We propose a speed control scheme of the exoskeleton from the aforementioned signals, which is executed in real time, achieving a reduction of the biceps muscle activity up to 94%.

Original languageEnglish
Title of host publication4th IEEE Colombian Conference on Automatic Control
Subtitle of host publicationAutomatic Control as Key Support of Industrial Productivity, CCAC 2019 - Proceedings
EditorsJose Garcia-Tirado, Diego Munoz-Durango, Hernan Alvarez, Hector Botero-Castro
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538669624
DOIs
StatePublished - Oct 2019
Event4th IEEE Colombian Conference on Automatic Control, CCAC 2019 - Medellin, Colombia
Duration: 15 Oct 201918 Oct 2019

Publication series

Name4th IEEE Colombian Conference on Automatic Control: Automatic Control as Key Support of Industrial Productivity, CCAC 2019 - Proceedings

Conference

Conference4th IEEE Colombian Conference on Automatic Control, CCAC 2019
Country/TerritoryColombia
CityMedellin
Period15/10/1918/10/19

Keywords

  • Exoskeleton
  • Force augmentation
  • Myoelectric signals
  • Neural networks
  • Upper limb
  • Wavelet transform

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