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

Name: 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.

İsim	On the EMG-based torque estimation for humans coupled with a force-controlled elbow exoskeleton
Yazar	Ullauri, J. B., Petenel, L., Uğurlu, Regaip Barkan, Yamada, Y., Morimoto, J.
Basım Tarihi:	2015
Basım Yeri	- IEEE
Konu	Human torque prediction, EMG, GPR, PAM model
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-146737509-2
Kayıt Numarası	f982930d-b49b-488d-9a72-836097ea2731
Lokasyon	Mechanical Engineering
Tarih	2015
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	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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On the EMG-based torque estimation for humans coupled with a force-controlled elbow exoskeleton

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

Basım Tarihi 2015

Basım Yeri - IEEE

Konu Human torque prediction, EMG, GPR, PAM model

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-146737509-2

Kayıt Numarası f982930d-b49b-488d-9a72-836097ea2731

Lokasyon Mechanical Engineering

Tarih 2015

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

Örnek Metin 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