A Subject-Specific Kinematic Model to Predict Human Motion in Exoskeleton-Assisted Gait

Authors: Diego Torricelli Camilo Andrés Cortés Acosta Nerea Lete Urzelai Álvaro Bertelsen Simonetti José E. González Antonio J. del Ama Iris Dimbwadyo Juan C. Moreno Julián Flórez Esnal José Luis Pons

Date: 27.04.2018

Frontiers in Neurorobotics


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 to predict 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 predictions accuracy lower than 3.5 degrees globally, and around 1.5 degrees 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.

BIB_text

@Article {
title = {A Subject-Specific Kinematic Model to Predict Human Motion in Exoskeleton-Assisted Gait},
journal = {Frontiers in Neurorobotics},
volume = {12},
keywds = {
Benchmarking; Human-exoskeleton interaction; Joint Angle Estimation; Kinematic Misalignment; Rehabilitation; Skeletal Modelling; Walking; lower limb; wearable robot
}
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 to predict 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 predictions accuracy lower than 3.5 degrees globally, and around 1.5 degrees 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.


}
doi = {10.3389/fnbot.2018.00018},
date = {2018-04-27},
}
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