Mechanomyography-based muscle fatigue detection during electrically elicited cycling in patients with spinal cord injury

Patients with spinal cord injury (SCI) benefit from muscle training with functional electrical stimulation (FES). For safety reasons and to optimize training outcome, the fatigue state of the target muscle must be monitored. Detection of muscle fatigue from mel frequency cepstral coefficient (MFCC)...

Full description

Bibliographic Details
Main Authors:	Naeem, Jannatul, Hamzaid, Nur Azah, Islam, Md. Anamul, Azman, Amelia Wong, Bijak, Manfred
Format:	Article
Language:	English English English
Published:	Springer Verlag 2019
Subjects:	QP Physiology RD Surgery T Technology (General)
Online Access:	http://irep.iium.edu.my/76358/ http://irep.iium.edu.my/76358/ http://irep.iium.edu.my/76358/ http://irep.iium.edu.my/76358/1/76358_Mechanomyography-based%20muscle%20fatigue_article.pdf http://irep.iium.edu.my/76358/2/76358_Mechanomyography-based%20muscle%20fatigue_scopus.pdf http://irep.iium.edu.my/76358/3/76358_Mechanomyography-based%20muscle%20fatigue_wos.pdf

id	iium-76358
recordtype	eprints
spelling	iium-763582019-11-19T08:51:16Z http://irep.iium.edu.my/76358/ Mechanomyography-based muscle fatigue detection during electrically elicited cycling in patients with spinal cord injury Naeem, Jannatul Hamzaid, Nur Azah Islam, Md. Anamul Azman, Amelia Wong Bijak, Manfred QP Physiology RD Surgery T Technology (General) Patients with spinal cord injury (SCI) benefit from muscle training with functional electrical stimulation (FES). For safety reasons and to optimize training outcome, the fatigue state of the target muscle must be monitored. Detection of muscle fatigue from mel frequency cepstral coefficient (MFCC) feature of mechanomyographic (MMG) signal using support vector machine (SVM) classifier is a promising new approach. Five individuals with SCI performed FES cycling exercises for 30 min. MMG signals were recorded on the quadriceps muscle group (rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM)) and categorized into non-fatigued and fatigued muscle contractions for the first and last 10 min of the cycling session. For each subject, a total of 1800 contraction-related MMG signals were used to train the SVM classifier and another 300 signals were used for testing. The average classification accuracy (4-fold) of non-fatigued and fatigued state was 90.7% using MFCC feature, 74.5% using root mean square (RMS), and 88.8% with combined MFCC and RMS features. Inter-subject prediction accuracy suggested training and testing data to be based on a particular subject or large collection of subjects to improve fatigue prediction capacity. Springer Verlag 2019-06-19 Article PeerReviewed application/pdf en http://irep.iium.edu.my/76358/1/76358_Mechanomyography-based%20muscle%20fatigue_article.pdf application/pdf en http://irep.iium.edu.my/76358/2/76358_Mechanomyography-based%20muscle%20fatigue_scopus.pdf application/pdf en http://irep.iium.edu.my/76358/3/76358_Mechanomyography-based%20muscle%20fatigue_wos.pdf Naeem, Jannatul and Hamzaid, Nur Azah and Islam, Md. Anamul and Azman, Amelia Wong and Bijak, Manfred (2019) Mechanomyography-based muscle fatigue detection during electrically elicited cycling in patients with spinal cord injury. Medical and Biological Engineering and Computing, 57 (6). pp. 1199-1211. ISSN 0140-0118 E-ISSN 1741-0444 (Unpublished) https://link.springer.com/article/10.1007%2Fs11517-019-01949-4 10.1007/s11517-019-01949-4
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English English
topic	QP Physiology RD Surgery T Technology (General)
spellingShingle	QP Physiology RD Surgery T Technology (General) Naeem, Jannatul Hamzaid, Nur Azah Islam, Md. Anamul Azman, Amelia Wong Bijak, Manfred Mechanomyography-based muscle fatigue detection during electrically elicited cycling in patients with spinal cord injury
description	Patients with spinal cord injury (SCI) benefit from muscle training with functional electrical stimulation (FES). For safety reasons and to optimize training outcome, the fatigue state of the target muscle must be monitored. Detection of muscle fatigue from mel frequency cepstral coefficient (MFCC) feature of mechanomyographic (MMG) signal using support vector machine (SVM) classifier is a promising new approach. Five individuals with SCI performed FES cycling exercises for 30 min. MMG signals were recorded on the quadriceps muscle group (rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM)) and categorized into non-fatigued and fatigued muscle contractions for the first and last 10 min of the cycling session. For each subject, a total of 1800 contraction-related MMG signals were used to train the SVM classifier and another 300 signals were used for testing. The average classification accuracy (4-fold) of non-fatigued and fatigued state was 90.7% using MFCC feature, 74.5% using root mean square (RMS), and 88.8% with combined MFCC and RMS features. Inter-subject prediction accuracy suggested training and testing data to be based on a particular subject or large collection of subjects to improve fatigue prediction capacity.
format	Article
author	Naeem, Jannatul Hamzaid, Nur Azah Islam, Md. Anamul Azman, Amelia Wong Bijak, Manfred
author_facet	Naeem, Jannatul Hamzaid, Nur Azah Islam, Md. Anamul Azman, Amelia Wong Bijak, Manfred
author_sort	Naeem, Jannatul
title	Mechanomyography-based muscle fatigue detection during electrically elicited cycling in patients with spinal cord injury
title_short	Mechanomyography-based muscle fatigue detection during electrically elicited cycling in patients with spinal cord injury
title_full	Mechanomyography-based muscle fatigue detection during electrically elicited cycling in patients with spinal cord injury
title_fullStr	Mechanomyography-based muscle fatigue detection during electrically elicited cycling in patients with spinal cord injury
title_full_unstemmed	Mechanomyography-based muscle fatigue detection during electrically elicited cycling in patients with spinal cord injury
title_sort	mechanomyography-based muscle fatigue detection during electrically elicited cycling in patients with spinal cord injury
publisher	Springer Verlag
publishDate	2019
url	http://irep.iium.edu.my/76358/ http://irep.iium.edu.my/76358/ http://irep.iium.edu.my/76358/ http://irep.iium.edu.my/76358/1/76358_Mechanomyography-based%20muscle%20fatigue_article.pdf http://irep.iium.edu.my/76358/2/76358_Mechanomyography-based%20muscle%20fatigue_scopus.pdf http://irep.iium.edu.my/76358/3/76358_Mechanomyography-based%20muscle%20fatigue_wos.pdf
first_indexed	2023-09-18T21:47:55Z
last_indexed	2023-09-18T21:47:55Z
_version_	1777413574498451456

Mechanomyography-based muscle fatigue detection during electrically elicited cycling in patients with spinal cord injury

Similar Items