Robust real-time musculoskeletal modeling driven by electromyograms
File(s)durandau 2017.pdf (1.04 MB)
Accepted version
Author(s)
Durandau, G
Farina, D
Sartori, M
Type
Journal Article
Abstract
Objective: Current clinical biomechanics involves lengthy data acquisition and time-consuming offline analyses and current biomechanical models cannot be used for real-time control in man-machine interfaces. We developed a method that enables online analysis of neuromusculoskeletal function in vivo in the intact human. Methods: We used electromyography (EMG)-driven musculoskeletal modeling to simulate all transformations from muscle excitation onset (EMGs) to mechanical moment production around multiple lower-limb degrees of freedom (DOFs). We developed a calibration algorithm that enables adjusting musculoskeletal model parameters specifically to an individual’s anthropometry and force-generating capacity. We incorporated the modeling paradigm into a computationally efficient, generic framework that can be interfaced in real-time with any movement data collection system. Results: The framework demonstrated the ability of computing forces in 13 lower-limb muscle-tendon units and resulting moments about three joint DOFs simultaneously in real-time. Remarkably, it was capable of extrapolating beyond calibration conditions, i.e. predicting accurate joint moments during six unseen tasks and one unseen DOF. Conclusion: The proposed framework can dramatically reduce evaluation latency in current clinical biomechanics and open up new avenues for establishing prompt and personalized treatments, as well as for establishing natural interfaces between patients and rehabilitation systems. Significance: The integration of EMG with numerical modeling will enable simulating realistic neuromuscular strategies in conditions including muscular/orthopedic deficit, which could not be robustly simulated via pure modeling formulations. This will enable translation to clinical settings and development of healthcare technologies including real-time bio-feedback of internal mechanical forces and direct patient-machine interfacing.
Date Issued
2017-05-12
Date Acceptance
2017-04-24
ISSN
1558-2531
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Start Page
556
End Page
564
Journal / Book Title
IEEE Transactions on Biomedical Engineering
Volume
65
Issue
3
Copyright Statement
© 20xx 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
Subjects
0903 Biomedical Engineering
0906 Electrical And Electronic Engineering
0801 Artificial Intelligence And Image Processing
Biomedical Engineering
Publication Status
Published