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

Sierotowicz M, Brusamento D, Schirrmeister B, Connan M, Bornmann J, Gonzalez-Vargas J, Castellini C (2022)

Publication Type: Journal article, Original article

Publication year: 2022

Journal

Frontiers in Robotics and AI Frontiers Media

Book Volume: 9

Article Number: 919370

DOI: 10.3389/frobt.2022.919370

Abstract

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.

Authors with CRIS profile

Marek Sierotowicz Assistive Intelligent Robotics Lab Claudio Castellini Assistive Intelligent Robotics Lab

Involved external institutions

Ottobock SE & Co KGaA DE Germany (DE) Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR) / German Aerospace Center DE Germany (DE)

How to cite

APA:

Sierotowicz, M., Brusamento, D., Schirrmeister, B., Connan, M., Bornmann, J., Gonzalez-Vargas, J., & Castellini, C. (2022). Unobtrusive, natural support control of an adaptive industrial exoskeleton using force myography. Frontiers in Robotics and AI, 9. https://dx.doi.org/10.3389/frobt.2022.919370

MLA:

Sierotowicz, Marek, et al. "Unobtrusive, natural support control of an adaptive industrial exoskeleton using force myography." Frontiers in Robotics and AI 9 (2022).

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