An anatomical atlas-based scaling study for quantifying muscle and hip joint contact forces in above and through-knee amputees using validated musculoskeletal modelling
File(s)
Accepted version
Author(s)
Toderita, Diana
Henson, David
Klemt, Christian
Ding, Ziyun
Bull, Anthony MJ
Type
Journal Article
Abstract
Objective: Customisation of musculoskeletal modelling using magnetic resonance imaging (MRI) significantly improves the model accuracy, but the process is time consuming and computationally intensive. This study hypothesizes that linear scaling to a lower limb amputee model with anthropometric similarity can accurately predict muscle and joint reaction forces. Methods: An MRI-based anatomical atlas, comprising 18 trans-femoral and through-knee traumatic lower limb amputee models, is developed. Gait data, using a 10-camera motion capture system with two force plates, and surface electromyography (EMG) data were collected. Muscle and hip joint contact forces were quantified using musculoskeletal modelling. The predicted muscle activations from the subject-specific models were validated using EMG recordings. Anthropometry based multiple linear regression models, which minimize errors in force predictions, are presented. Results: All predictions showed excellent (error interval c=00.15), very good (c=0.150.30) or good (c=0.300.45) similarity to the recorded EMG data, demonstrating that the models accurately computed muscle activations. The primary predictors of discrepancies in force predictions were differences in pelvis width (p<0.001), body mass index (BMI, p<0.001) and stump length to pelvis width ratio (p<0.001) between the respective individual and underlying dataset. Conclusion: Linear scaling to a model with the most similar pelvis width, BMI and stump length to pelvis width ratio results in modelling outcomes with minimal errors. Significance: This study provides robust tools to perform accurate analyses of musculoskeletal mechanics for high-functioning lower limb military amputees, thus facilitating the further understanding and improvement of the amputee's function.
Date Issued
2021-11-01
Date Acceptance
2021-04-01
Citation
IEEE Transactions on Biomedical Engineering, 2021, 68 (11), pp.3447-3456
ISSN
0018-9294
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Start Page
3447
End Page
3456
Journal / Book Title
IEEE Transactions on Biomedical Engineering
Volume
68
Issue
11
Copyright Statement
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
The Royal British Legion
Identifier
https://ieeexplore.ieee.org/document/9411694
Grant Number
BMPF_P60304
Subjects
Biomedical Engineering
0801 Artificial Intelligence and Image Processing
0903 Biomedical Engineering
0906 Electrical and Electronic Engineering
Publication Status
Published
Date Publish Online
2021-04-22