A Subject-Specific Kinematic Model to Predict Human Motion in Exoskeleton-Assisted Gait
The relative motion between human and exoskeleton is a crucial factor that has remarkable consequences on the efficiency, reliability and safety of human-robot interaction. Unfortunately, its quantitative assessment has been largely overlooked in the literature. Here, we present a methodology that a...
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Published in | Frontiers in neurorobotics Vol. 12; p. 18 |
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Main Authors | , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
Frontiers Research Foundation
27.04.2018
Frontiers Media S.A |
Subjects | |
Online Access | Get full text |
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Summary: | The relative motion between human and exoskeleton is a crucial factor that has remarkable consequences on the efficiency, reliability and safety of human-robot interaction. Unfortunately, its quantitative assessment has been largely overlooked in the literature. Here, we present a methodology that allows predicting the motion of the human joints from the knowledge of the angular motion of the exoskeleton frame. Our method combines a subject-specific skeletal model with a kinematic model of a lower limb exoskeleton (H2, Technaid), imposing specific kinematic constraints between them. To calibrate the model and validate its ability to predict the relative motion in a subject-specific way, we performed experiments on seven healthy subjects during treadmill walking tasks. We demonstrate a prediction accuracy lower than 3.5° globally, and around 1.5° at the hip level, which represent an improvement up to 66% compared to the traditional approach assuming no relative motion between the user and the exoskeleton. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Reviewed by: William Zev Rymer, Rehabilitation Institute of Chicago, United States; Fan Gao, University of Kentucky, United States These authors have contributed equally to this work. Edited by: Guang Chen, Tongji University, China |
ISSN: | 1662-5218 1662-5218 |
DOI: | 10.3389/fnbot.2018.00018 |