Title |
A Subject-Specific Kinematic Model to Predict Human Motion in Exoskeleton-Assisted Gait
|
---|---|
Published in |
Frontiers in Neurorobotics, April 2018
|
DOI | 10.3389/fnbot.2018.00018 |
Pubmed ID | |
Authors |
Diego Torricelli, Camilo Cortés, Nerea Lete, Álvaro Bertelsen, Jose E. Gonzalez-Vargas, Antonio J. del-Ama, Iris Dimbwadyo, Juan C. Moreno, Julian Florez, Jose L. Pons |
Abstract |
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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