Unobtrusive, natural support control of an adaptive industrial exoskeleton using force myography - Sierotowicz, Marek; Brusamento, Donato; Schirrmeister, Benjamin; Connan, Mathilde; Bornmann, Jonas; Gonzalez-Vargas, Jose; Castellini, Claudio (Frontiers in Robotics and AI, 2022)
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 show 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 2kg normalized by the baseline when lifting no mass.