Joint torque estimation model of surface electromyography(sEMG) based on swarm intelligence algorithm for robotic assistive device

Ku Nurhanim; I. Elamvazuthi; P. Vasant; T. Ganesan; S. Parasuraman; M. K.A. Ahamed Khan

doi:10.1016/j.procs.2014.11.049

Joint torque estimation model of surface electromyography(sEMG) based on swarm intelligence algorithm for robotic assistive device

Ku Nurhanim, I. Elamvazuthi, P. Vasant, T. Ganesan, S. Parasuraman, M. K.A. Ahamed Khan

Malaysia School of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

13 Citations (Scopus)

Abstract

The conventional robotic assistive device was based on pre-programmed functions by the robot expert. This makes it difficult for stroke patients use it effectively due to difficulty of torque setting that is suitable for the user movement. Electromyography (EMG) signal measures the electrical signal of muscle contraction. The EMG-based robotics assistive technology would enable the stroke patients to control the robot movement according to the user's own strength of natural movement. This paper discusses the mapping of surface electromyography signals (sEMG) to torque for robotic rehabilitation. Particle swarm optimization (PSO) has been applied as a control algorithm for a number of selected mathematical models. sEMG signals were determined as input data to the mathematical model where parameters of the mathematical model were optimized using PSO. Hence, the good correlated estimated torque as output was obtained.

Original language	English
Title of host publication	International Symposium on Medical and Rehabilitation Robotics and Instrumentation, MRRI 2013
Pages	175-182
Number of pages	8
Volume	42
Edition	C
DOIs	https://doi.org/10.1016/j.procs.2014.11.049
Publication status	Published - 2014
Event	International Symposium on Medical and Rehabilitation Robotics and Instrumentation 2013 - Shah Alam, Malaysia Duration: 2 Dec 2013 → 4 Dec 2013 https://www.sciencedirect.com/journal/procedia-computer-science/vol/42/suppl/C (Proceedings)

Publication series

Name	Procedia Computer Science
Publisher	Elsevier
ISSN (Print)	1877-0509

Conference

Conference	International Symposium on Medical and Rehabilitation Robotics and Instrumentation 2013
Abbreviated title	MRRI 2013
Country/Territory	Malaysia
City	Shah Alam
Period	2/12/13 → 4/12/13
Internet address	https://www.sciencedirect.com/journal/procedia-computer-science/vol/42/suppl/C (Proceedings)

Keywords

Biomechanics human motion
Electromyography
Feature extraction
Joint torque estimation model
Particle swarm optimization
Robot rehabilitation

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10.1016/j.procs.2014.11.049

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Nurhanim, K., Elamvazuthi, I., Vasant, P., Ganesan, T., Parasuraman, S., & Ahamed Khan, M. K. A. (2014). Joint torque estimation model of surface electromyography(sEMG) based on swarm intelligence algorithm for robotic assistive device. In International Symposium on Medical and Rehabilitation Robotics and Instrumentation, MRRI 2013 (C ed., Vol. 42, pp. 175-182). (Procedia Computer Science). https://doi.org/10.1016/j.procs.2014.11.049

@inproceedings{eb99679eaaa042ebaf350aee253a471c,

title = "Joint torque estimation model of surface electromyography(sEMG) based on swarm intelligence algorithm for robotic assistive device",

abstract = "The conventional robotic assistive device was based on pre-programmed functions by the robot expert. This makes it difficult for stroke patients use it effectively due to difficulty of torque setting that is suitable for the user movement. Electromyography (EMG) signal measures the electrical signal of muscle contraction. The EMG-based robotics assistive technology would enable the stroke patients to control the robot movement according to the user's own strength of natural movement. This paper discusses the mapping of surface electromyography signals (sEMG) to torque for robotic rehabilitation. Particle swarm optimization (PSO) has been applied as a control algorithm for a number of selected mathematical models. sEMG signals were determined as input data to the mathematical model where parameters of the mathematical model were optimized using PSO. Hence, the good correlated estimated torque as output was obtained.",

keywords = "Biomechanics human motion, Electromyography, Feature extraction, Joint torque estimation model, Particle swarm optimization, Robot rehabilitation",

author = "Ku Nurhanim and I. Elamvazuthi and P. Vasant and T. Ganesan and S. Parasuraman and {Ahamed Khan}, {M. K.A.}",

note = "Publisher Copyright: {\textcopyright} 2014 The Authors. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.; International Symposium on Medical and Rehabilitation Robotics and Instrumentation 2013, MRRI 2013 ; Conference date: 02-12-2013 Through 04-12-2013",

year = "2014",

doi = "10.1016/j.procs.2014.11.049",

language = "English",

volume = "42",

series = "Procedia Computer Science",

publisher = "Elsevier",

pages = "175--182",

booktitle = "International Symposium on Medical and Rehabilitation Robotics and Instrumentation, MRRI 2013",

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url = "https://www.sciencedirect.com/journal/procedia-computer-science/vol/42/suppl/C",

}

Nurhanim, K, Elamvazuthi, I, Vasant, P, Ganesan, T, Parasuraman, S & Ahamed Khan, MKA 2014, Joint torque estimation model of surface electromyography(sEMG) based on swarm intelligence algorithm for robotic assistive device. in International Symposium on Medical and Rehabilitation Robotics and Instrumentation, MRRI 2013. C edn, vol. 42, Procedia Computer Science, pp. 175-182, International Symposium on Medical and Rehabilitation Robotics and Instrumentation 2013, Shah Alam, Malaysia, 2/12/13. https://doi.org/10.1016/j.procs.2014.11.049

Joint torque estimation model of surface electromyography(sEMG) based on swarm intelligence algorithm for robotic assistive device. / Nurhanim, Ku; Elamvazuthi, I.; Vasant, P. et al.
International Symposium on Medical and Rehabilitation Robotics and Instrumentation, MRRI 2013. Vol. 42 C. ed. 2014. p. 175-182 (Procedia Computer Science).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

TY - GEN

T1 - Joint torque estimation model of surface electromyography(sEMG) based on swarm intelligence algorithm for robotic assistive device

AU - Nurhanim, Ku

AU - Elamvazuthi, I.

AU - Vasant, P.

AU - Ganesan, T.

AU - Parasuraman, S.

AU - Ahamed Khan, M. K.A.

PY - 2014

Y1 - 2014

N2 - The conventional robotic assistive device was based on pre-programmed functions by the robot expert. This makes it difficult for stroke patients use it effectively due to difficulty of torque setting that is suitable for the user movement. Electromyography (EMG) signal measures the electrical signal of muscle contraction. The EMG-based robotics assistive technology would enable the stroke patients to control the robot movement according to the user's own strength of natural movement. This paper discusses the mapping of surface electromyography signals (sEMG) to torque for robotic rehabilitation. Particle swarm optimization (PSO) has been applied as a control algorithm for a number of selected mathematical models. sEMG signals were determined as input data to the mathematical model where parameters of the mathematical model were optimized using PSO. Hence, the good correlated estimated torque as output was obtained.

AB - The conventional robotic assistive device was based on pre-programmed functions by the robot expert. This makes it difficult for stroke patients use it effectively due to difficulty of torque setting that is suitable for the user movement. Electromyography (EMG) signal measures the electrical signal of muscle contraction. The EMG-based robotics assistive technology would enable the stroke patients to control the robot movement according to the user's own strength of natural movement. This paper discusses the mapping of surface electromyography signals (sEMG) to torque for robotic rehabilitation. Particle swarm optimization (PSO) has been applied as a control algorithm for a number of selected mathematical models. sEMG signals were determined as input data to the mathematical model where parameters of the mathematical model were optimized using PSO. Hence, the good correlated estimated torque as output was obtained.

KW - Biomechanics human motion

KW - Electromyography

KW - Feature extraction

KW - Joint torque estimation model

KW - Particle swarm optimization

KW - Robot rehabilitation

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M3 - Conference Paper

AN - SCOPUS:84925609274

VL - 42

T3 - Procedia Computer Science

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EP - 182

BT - International Symposium on Medical and Rehabilitation Robotics and Instrumentation, MRRI 2013

T2 - International Symposium on Medical and Rehabilitation Robotics and Instrumentation 2013

Y2 - 2 December 2013 through 4 December 2013

ER -

Nurhanim K, Elamvazuthi I, Vasant P, Ganesan T, Parasuraman S, Ahamed Khan MKA. Joint torque estimation model of surface electromyography(sEMG) based on swarm intelligence algorithm for robotic assistive device. In International Symposium on Medical and Rehabilitation Robotics and Instrumentation, MRRI 2013. C ed. Vol. 42. 2014. p. 175-182. (Procedia Computer Science). doi: 10.1016/j.procs.2014.11.049

Joint torque estimation model of surface electromyography(sEMG) based on swarm intelligence algorithm for robotic assistive device

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Keywords

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