Validation and use of a musculoskeletal gait model to study the role of functional electrical stimulation
File(s)FINAL VERSION.pdf (617.03 KB)
Accepted version
Author(s)
Ding, Z
Azmi, Nur Liyana
Bull, Anthony MJ
Type
Journal Article
Abstract
Objective: Musculoskeletal modeling has been used to predict the effect of functional electrical stimulation (FES) on the mechanics of the musculoskeletal system. However, validation of the resulting muscle activations due to FES is challenging as conventional electromyography (EMG) recording of signals from the stimulated muscle is affected by stimulation artefacts. A validation approach using a combination of musculoskeletal modeling and EMG was proposed, whereby the effect on nonstimulated muscles is assessed using both techniques. The aim is to quantify the effect of FES on biceps femoris long head (BFLH) and validate this directly against EMG of gluteus maximus (GMAX). The hypotheses are that GMAX activation correlates with BFLH activation; and the muscle activation during FES gait can be predicted using musculoskeletal modeling.
Methods: Kinematics, kinetics, and EMG of healthy subjects were measured under four walking conditions (normal walking followed by FES walking with three levels of BFLH stimulation). Measured kinematics and kinetics served as inputs to the musculoskeletal model.
Results: Strong positive correlations were found between GMAX activation and BFLH activation in early stance peak (R = 0.78, p = 0.002) and impulse (R = 0.63, p = 0.021). The modeled peak and impulse of GMAX activation increased with EMG peak (p <; 0.001) and impulse (p = 0.021).
Conclusion: Musculoskeletal modeling can be used reliably to quantify the effect of FES in a healthy gait.
Significance: The validation approach using EMG and musculoskeletal modeling developed and tested can potentially be applied to the use of FES for other muscles and activities.
Methods: Kinematics, kinetics, and EMG of healthy subjects were measured under four walking conditions (normal walking followed by FES walking with three levels of BFLH stimulation). Measured kinematics and kinetics served as inputs to the musculoskeletal model.
Results: Strong positive correlations were found between GMAX activation and BFLH activation in early stance peak (R = 0.78, p = 0.002) and impulse (R = 0.63, p = 0.021). The modeled peak and impulse of GMAX activation increased with EMG peak (p <; 0.001) and impulse (p = 0.021).
Conclusion: Musculoskeletal modeling can be used reliably to quantify the effect of FES in a healthy gait.
Significance: The validation approach using EMG and musculoskeletal modeling developed and tested can potentially be applied to the use of FES for other muscles and activities.
Date Issued
2019-03-01
Date Acceptance
2018-08-10
Citation
IEEE Transactions on Biomedical Engineering, 2019, 66 (3), pp.892-897
ISSN
0018-9294
Publisher
Institute of Electrical and Electronics Engineers
Start Page
892
End Page
897
Journal / Book Title
IEEE Transactions on Biomedical Engineering
Volume
66
Issue
3
Copyright Statement
© 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution
requires IEEE permission. See http://www.ieee.org/publications standards/publications/rights/index.html for more information.
requires IEEE permission. See http://www.ieee.org/publications standards/publications/rights/index.html for more information.
Sponsor
The Royal British Legion
Identifier
https://ieeexplore.ieee.org/document/8452973
Grant Number
Centre for Blast Injury Studie
Subjects
Science & Technology
Technology
Engineering, Biomedical
Engineering
Functional electrical stimulation
musculoskeletal modelling
MUSCLE CONTRIBUTIONS
WALKING
FES
COORDINATION
CRITERION
FORCES
Adult
Electric Stimulation
Electromyography
Female
Gait
Humans
Male
Models, Biological
Muscle, Skeletal
Signal Processing, Computer-Assisted
Young Adult
Muscle, Skeletal
Humans
Electromyography
Gait
Electric Stimulation
Models, Biological
Signal Processing, Computer-Assisted
Adult
Female
Male
Young Adult
Biomedical Engineering
0801 Artificial Intelligence and Image Processing
0903 Biomedical Engineering
0906 Electrical and Electronic Engineering
Publication Status
Published
Date Publish Online
2018-08-31