Analysis of Human Whole-Body Joint Torques During Overhead Work With a Passive Exoskeleton

Autor: Claudia Latella, Yeshasvi Tirupachuri, Luca Tagliapietra, Lorenzo Rapetti, Benjamin Schirrmeister, Jonas Bornmann, Dasa Gorjan, Jernej Camernik, Pauline Maurice, Lars Fritzsche, Jose Gonzalez-Vargas, Serena Ivaldi, Jan Babic, Francesco Nori, Daniele Pucci
Přispěvatelé: Istituto Italiano di Tecnologia (IIT), Ottobock SE & Co. KGaA, JSI (JSI Ljubljana), Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment (LARSEN), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), imk automotive GmbH, European Project: 731540,H2020,An.Dy(2017), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)
Rok vydání: 2022
Předmět:
Zdroj: IEEE Transactions on Human-Machine Systems
IEEE Transactions on Human-Machine Systems, IEEE, In press
IEEE Transactions on Human-Machine Systems, In press, ⟨10.1109/THMS.2021.3128892⟩
ISSN: 2168-2305
2168-2291
Popis: International audience; Overhead work is classified as one of the major risk factors for the onset of shoulder work-related musculoskeletal disorders (WMSDs) and muscle fatigue. Upper-limb exoskeletons can be used to assist workers during the execution of industrial overhead tasks to prevent such disorders. Twelve novice participants have been equipped with inertial and force/torque sensors to simultaneously estimate the whole-body kinematics and the joint torques (i.e., internal articular stress) by means of a probabilistic estimator while performing an overhead taskwith a pointing tool. An evaluation has been performed to analyze the effect at the whole-body level by considering the conditions of wearing and not-wearing PAEXO, a passive exoskeleton for upper-limb support during overhead work. Results point out that PAEXO provides a reduction of the whole-body joint effort across the experimental task blocks (from 66% to 86%). Moreover, the analysis along with 5 different body areas shows that i) the exoskeleton provides support at the human shoulders by reducing the joint effort at the targeted limbs and ii) that part of theinternal wrenches is intuitively transferred from the upper body to the thighs and legs, which is shown with an increment of the torques at the legs joints. The promising outcomes show that the probabilistic estimation algorithm can be used as a validation metric to quantitatively assess PAEXO performances, paving thus the way for the next challenging milestone, such as the optimization of the human joint torques via adaptive exoskeleton control.
Databáze: OpenAIRE
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