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

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Predictive Exoskeleton Control for Arm-Motion Augmentation Based on Probabilistic Movement Primitives Combined with a Flow Controller. / Jamsek, Marko; Kunavar, Tjasa; Bobek, Urban; Rueckert, Elmar; Babic, Jan.

in: IEEE robotics and automation letters, Jahrgang 6.2021, Nr. 3, 9387088, 25.03.2021, S. 4417-4424.

Publikationen: Beitrag in FachzeitschriftArtikelForschung(peer-reviewed)

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@article{ebb3c70a616848a4a4e6f93e403f7d73,
title = "Predictive Exoskeleton Control for Arm-Motion Augmentation Based on Probabilistic Movement Primitives Combined with a Flow Controller",
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.",
keywords = "Physical human-robot interaction, physically assistive devices, prosthetics and exoskeletons",
author = "Marko Jamsek and Tjasa Kunavar and Urban Bobek and Elmar Rueckert and Jan Babic",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.",
year = "2021",
month = mar,
day = "25",
doi = "10.1109/LRA.2021.3068892",
language = "English",
volume = "6.2021",
pages = "4417--4424",
journal = " IEEE robotics and automation letters",
issn = "2377-3766",
publisher = "Institute of Electrical and Electronics Engineers",
number = "3",

}

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TY - JOUR

T1 - Predictive Exoskeleton Control for Arm-Motion Augmentation Based on Probabilistic Movement Primitives Combined with a Flow Controller

AU - Jamsek, Marko

AU - Kunavar, Tjasa

AU - Bobek, Urban

AU - Rueckert, Elmar

AU - Babic, Jan

N1 - Publisher Copyright: © 2016 IEEE.

PY - 2021/3/25

Y1 - 2021/3/25

N2 - 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.

AB - 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.

KW - Physical human-robot interaction

KW - physically assistive devices

KW - prosthetics and exoskeletons

UR - http://www.scopus.com/inward/record.url?scp=85103251228&partnerID=8YFLogxK

U2 - 10.1109/LRA.2021.3068892

DO - 10.1109/LRA.2021.3068892

M3 - Article

AN - SCOPUS:85103251228

VL - 6.2021

SP - 4417

EP - 4424

JO - IEEE robotics and automation letters

JF - IEEE robotics and automation letters

SN - 2377-3766

IS - 3

M1 - 9387088

ER -