Predictive Exoskeleton Control for Arm-Motion Augmentation Based on Probabilistic Movement Primitives Combined with a Flow Controller

Marko Jamsek, Tjasa Kunavar, Urban Bobek, Elmar Rueckert, Jan Babic

Publikation: Beitrag in FachzeitschriftArtikelForschungBegutachtung

Abstract

There are many work-related repetitive tasks where the application of exoskeletons could significantly reduce the physical effort by assisting the user in moving the arms towards the desired location in space. To make such control more user acceptable, the controller should be able to predict the motion of the user and act accordingly. This letter presents an exoskeleton control method that utilizes probabilistic movement primitives to generate predictions of user movements in real-time. These predictions are used in a flow controller, which represents a novel velocity-field-based exoskeleton control approach to provide assistance to the user in a predictive way. We evaluated our approach with a haptic robot, where a group of twelve participants had to perform movements towards different target locations in the frontal plane. We tested whether we could generalize the predictions for new and unknown target locations whilst providing assistance to the user without changing their kinematic parameters. The evaluation showed that we could accurately predict user movement intentions while at the same time significantly decrease the overall physical effort exerted by the participants to achieve the task.
OriginalspracheEnglisch
Aufsatznummer9387088
Seiten (von - bis)4417-4424
Seitenumfang8
Fachzeitschrift IEEE robotics and automation letters
Jahrgang6.2021
Ausgabenummer3
DOIs
PublikationsstatusElektronische Veröffentlichung vor Drucklegung. - 25 März 2021

Bibliographische Notiz

Funding Information:
Manuscript received December 21, 2020; accepted February 21, 2021. Date of publication March 25, 2021; date of current version April 9, 2021. This letter was recommended for publication by Associate Editor D. Losey and Editor G. Venture upon evaluation of the reviewers’ comments. This work was supported in part by the European Union’s Horizon 2020 through the AnDy Project (Contract nr. 731540); by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - No #430054590 (TRAIN); and in part by the Slovenian Research Agency (Research core funding no. P2-0076). (Corresponding author: Marko Jamšek.) Marko Jamšek and Tjaša Kunavar are with the Laboratory of Neuromechanics, and Biorobotics, Department for Automation, Biocybernetics, and Robotics, Jožef Stefan Institute, 1000 Ljubljana, Slovenia and also with the Jožef Stefan International Postgraduate School, Jamova cesta 39, 1000 Ljubljana, Slovenia (e-mail: marko.jamsek@ijs.si; tjasa.kunavar@ijs.si).

Publisher Copyright:
© 2016 IEEE.

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