Validation and use of a musculoskeletal gait model to study the role of functional electrical stimulation
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 s...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English English English |
| Published: |
IEEE Computer Society Malaysia
2019
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/71743/ http://irep.iium.edu.my/71743/ http://irep.iium.edu.my/71743/ http://irep.iium.edu.my/71743/1/71743_Validation%20and%20Use%20of%20a%20Musculoskeletal_article.pdf http://irep.iium.edu.my/71743/2/71743_Validation%20and%20Use%20of%20a%20Musculoskeletal_wos.pdf http://irep.iium.edu.my/71743/3/71743_Validation%20and%20Use%20of%20a%20Musculoskeletal_scopus.pdf |
| Summary: | 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. |
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