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Unobtrusive, natural support control of an adaptive industrial exoskeleton using force myography

Sierotowicz, Marek and Brusamento, Donato and Schirrmeister, Benjamin and Connan, Mathilde and Bornmann, Jonas and Gonzalez-Vargas, Jose and Castellini, Claudio (2022) Unobtrusive, natural support control of an adaptive industrial exoskeleton using force myography. Frontiers in Robotics and AI, 9. Frontiers Media S.A. doi: 10.3389/frobt.2022.919370. ISSN 2296-9144.

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Official URL: https://dx.doi.org/10.3389/frobt.2022.919370


Repetitive or tiring tasks and movements during manual work can lead to serious musculoskeletal disorders and, consequently, to monetary damage for both the worker and the employer. Among the most common of these tasks is overhead working while operating a heavy tool, such as drilling, painting, and decorating. In such scenarios, it is desirable to provide adaptive support in order to take some of the load off the shoulder joint as needed. However, even to this day, hardly any viable approaches have been tested, which could enable the user to control such assistive devices naturally and in real time. Here, we present and assess the adaptive Paexo Shoulder exoskeleton, an unobtrusive device explicitly designed for this kind of industrial scenario, which can provide a variable amount of support to the shoulders and arms of a user engaged in overhead work. The adaptive Paexo Shoulder exoskeleton is controlled through machine learning applied to force myography. The controller is able to determine the lifted mass and provide the required support in real time. Twelve subjects joined a user study comparing the Paexo driven through this adaptive control to the Paexo locked in a fixed level of support. The results showed that the machine learning algorithm can successfully adapt the level of assistance to the lifted mass. Specifically, adaptive assistance can sensibly reduce the muscle activity’s sensitivity to the lifted mass, with an observed relative reduction of up to 31% of the muscular activity observed when lifting 2 kg normalized by the baseline when lifting no mass.

Item URL in elib:https://elib.dlr.de/192927/
Document Type:Article
Title:Unobtrusive, natural support control of an adaptive industrial exoskeleton using force myography
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Sierotowicz, MarekUNSPECIFIEDhttps://orcid.org/0000-0001-8040-6438UNSPECIFIED
Schirrmeister, BenjaminFriedrich-Alexander-Universität Erlangen-NürnbergUNSPECIFIEDUNSPECIFIED
Connan, MathildeUNSPECIFIEDhttps://orcid.org/0000-0001-6735-4194UNSPECIFIED
Bornmann, JonasFriedrich-Alexander-Universität Erlangen-NürnbergUNSPECIFIEDUNSPECIFIED
Gonzalez-Vargas, JoseFriedrich-Alexander-Universität Erlangen-NürnbergUNSPECIFIEDUNSPECIFIED
Castellini, ClaudioUNSPECIFIEDhttps://orcid.org/0000-0002-7346-2180UNSPECIFIED
Date:12 September 2022
Journal or Publication Title:Frontiers in Robotics and AI
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
Publisher:Frontiers Media S.A
Keywords:force myography, machine learning, adaptive support, exoskeletons, human–machine interaction
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Robotics
DLR - Research area:Raumfahrt
DLR - Program:R RO - Robotics
DLR - Research theme (Project):R - Terrestrial Assistance Robotics
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013)
Deposited By: Sierotowicz, Marek
Deposited On:09 Jan 2023 17:00
Last Modified:09 Jan 2023 17:00

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