A Subject-Specific Kinematic Model to Predict Human Motion in Exoskeleton-Assisted Gait
Autores: Diego Torricelli Nerea Lete Urzelai José E. González Antonio J. del Ama Iris Dimbwadyo Juan C. Moreno José Luis Pons
Fecha: 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
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},
}
