Calibrating an EMG-Driven Muscle Model and a Regression Model to Estimate Moments Generated Actively by Back Muscles for Controlling an Actuated Exoskeleton with Limited Data

Ali Tabasi*, Maria Lazzaroni, Niels P. Brouwer, Idsart Kingma, Wietse van Dijk, Michiel P. de Looze, Stefano Toxiri, Jesús Ortiz, Jaap H. van Dieën

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

Abstract

Estimation of low-back load can be used to determine the assistance to be provided by an actuated back-support exoskeleton. To this end, an EMG-driven muscle model and a regression model can be implemented. The goal of the regression model is to reduce the number of required sensors for load estimation. Both models need to be calibrated. This study aims to find the impacts of limiting calibration data on low-back loading estimation through these models.

Original languageEnglish
Title of host publicationWearable Robotics: Challenges and Trends
Subtitle of host publicationProceedings of the 5th International Symposium on Wearable Robotics, WeRob2020, and of WearRAcon Europe 2020, October 13–16, 2020
EditorsJuan C. Moreno, Jawad Masood, Urs Schneider, Christophe Maufroy, Jose L. Pons
PublisherSpringer Science and Business Media Deutschland GmbH
Pages401-405
Number of pages5
ISBN (Electronic)9783030695477
ISBN (Print)9783030695460, 9783030695491
DOIs
Publication statusPublished - 2022

Publication series

NameBiosystems and Biorobotics
Volume27
ISSN (Print)2195-3562
ISSN (Electronic)2195-3570

Bibliographical note

Funding Information:
Acknowledgements This research was funded by the i-Botics Early Research Program of TNO (the Netherlands Organization for Applied Scientific Research). Additionally, this work was supported by the Dutch Research Council (NWO), program ‘perspectief’ (project P16-05).

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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