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On the EMG-based torque estimation for humans coupled with a force-controlled elbow exoskeleton

Title	On the EMG-based torque estimation for humans coupled with a force-controlled elbow exoskeleton
Author	Ullauri, J. B., Petenel, L., Uğurlu, Regaip Barkan, Yamada, Y., Morimoto, J.
Publication Date:	2015
Publication Place	- IEEE
Subject	Human torque prediction, EMG, GPR, PAM model
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	978-146737509-2
Record ID	f982930d-b49b-488d-9a72-836097ea2731
Library Location	Mechanical Engineering
Date	2015
Notes	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text	Exoskeletons are successful at supporting human motion only when the necessary amount of power is provided at the right time. Exoskeleton control based on EMG signals can be utilized to command the required amount of support in real-time. To this end, one needs to map human muscle activity to the desired task-specific exoskeleton torques. In order to achieve such mapping, this paper analyzes two distinct methods to estimate the human-elbow-joint torque based on the related muscle activity. The first model is adopted from pneumatic artificial muscles (PAMs). The second model is based on a machine learning method known as Gaussian Process Regression (GPR). The performance of both approaches were assessed based on their ability to estimate the elbow-joint torque of two able-bodied subjects using EMG signals that were collected from biceps and triceps muscles. The experiments suggest that the GPR-based approach provides relatively more favorable predictions.
DOI	10.1109/ICAR.2015.7251472

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Özyeğin University

On the EMG-based torque estimation for humans coupled with a force-controlled elbow exoskeleton

Author Ullauri, J. B., Petenel, L., Uğurlu, Regaip Barkan, Yamada, Y., Morimoto, J.

Publication Date 2015

Publication Place - IEEE

Subject Human torque prediction, EMG, GPR, PAM model

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 978-146737509-2

Record ID f982930d-b49b-488d-9a72-836097ea2731

Library Location Mechanical Engineering

Date 2015

Notes Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Sample Text Exoskeletons are successful at supporting human motion only when the necessary amount of power is provided at the right time. Exoskeleton control based on EMG signals can be utilized to command the required amount of support in real-time. To this end, one needs to map human muscle activity to the desired task-specific exoskeleton torques. In order to achieve such mapping, this paper analyzes two distinct methods to estimate the human-elbow-joint torque based on the related muscle activity. The first model is adopted from pneumatic artificial muscles (PAMs). The second model is based on a machine learning method known as Gaussian Process Regression (GPR). The performance of both approaches were assessed based on their ability to estimate the elbow-joint torque of two able-bodied subjects using EMG signals that were collected from biceps and triceps muscles. The experiments suggest that the GPR-based approach provides relatively more favorable predictions.

DOI 10.1109/ICAR.2015.7251472