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

adaptive support exoskeletons force myography human–machine interaction machine learning

Journal

Frontiers in robotics and AI
ISSN: 2296-9144
Titre abrégé: Front Robot AI
Pays: Switzerland
ID NLM: 101749350

Informations de publication

Date de publication:
2022
Historique:
received: 13 04 2022
accepted: 26 07 2022
entrez: 29 9 2022
pubmed: 30 9 2022
medline: 30 9 2022
Statut: epublish

Résumé

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.

Identifiants

pubmed: 36172305
doi: 10.3389/frobt.2022.919370
pii: 919370
pmc: PMC9510611
doi:

Types de publication

Journal Article

Langues

eng

Pagination

919370

Informations de copyright

Copyright © 2022 Sierotowicz, Brusamento, Schirrmeister, Connan, Bornmann, Gonzalez-Vargas and Castellini.

Déclaration de conflit d'intérêts

Authors BS, JB, and JG-V were employed by the Ottobock SE and Co. KGaA. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Marek Sierotowicz (M)

Institute of Robotics and Mechatronics, German Aerospace Center (DLR), Erlangen, Germany.
Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.

Donato Brusamento (D)

Institute of Robotics and Mechatronics, German Aerospace Center (DLR), Erlangen, Germany.

Benjamin Schirrmeister (B)

Global Research, Ottobock SE and Co. KGaA, Duderstadt, Germany.

Mathilde Connan (M)

Institute of Robotics and Mechatronics, German Aerospace Center (DLR), Erlangen, Germany.

Jonas Bornmann (J)

Global Research, Ottobock SE and Co. KGaA, Duderstadt, Germany.

Jose Gonzalez-Vargas (J)

Global Research, Ottobock SE and Co. KGaA, Duderstadt, Germany.

Claudio Castellini (C)

Institute of Robotics and Mechatronics, German Aerospace Center (DLR), Erlangen, Germany.
Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.

Classifications MeSH