Evaluation of Synergy Extrapolation for Predicting Unmeasured Muscle Excitations from Measured Muscle Synergies

Abstract

Electromyography (EMG)-driven musculoskeletal modeling relies on high-quality measurements of muscle electrical activity to estimate muscle forces. However, a critical challenge for practical deployment of this approach is missing EMG data from muscles that contribute substantially to joint moments. This situation may arise due to either the inability to measure deep muscles with surface electrodes or the lack of a sufficient number of EMG channels. Muscle synergy analysis (MSA) is a dimensionality reduction approach that decomposes a large number of muscle excitations into a small number of time-varying synergy excitations along with time-invariant synergy weights that define the contribution of each synergy excitation to all muscle excitations. This study evaluates how well missing muscle excitations can be predicted using synergy excitations extracted from muscles with available EMG data (henceforth called “synergy extrapolation” or SynX). The method was evaluated using a gait data set collected from a stroke survivor walking on an instrumented treadmill at self-selected and fastest-comfortable speeds. The evaluation process started with full calibration of a lower-body EMG-driven model using 16 measured EMG channels (collected using surface and fine wire electrodes) per leg. One fine wire EMG channel (either iliopsoas or adductor longus) was then treated as unmeasured. The synergy weights associated with the unmeasured muscle excitation were predicted by solving a nonlinear optimization problem where the errors between inverse dynamics and EMG-driven joint moments were minimized. The prediction process was performed for different synergy analysis algorithms (principal component analysis and non-negative matrix factorization), EMG normalization methods, and numbers of synergies. SynX performance was most influenced by the choice of synergy analysis algorithm and number of synergies. Principal component analysis with five or six synergies consistently predicted unmeasured muscle excitations the most accurately and with the greatest robustness to EMG normalization method. Furthermore, the associated joint moment matching accuracy was comparable to that produced by initial EMG-driven model calibration using all 16 EMG channels per leg. SynX may facilitate the assessment of human neuromuscular control and biomechanics when important EMG signals are missing.

A muscle synergy-based method to estimate muscle activation patterns of children with cerebral palsy using data collected from typically developing children

“…Furthermore, it remains unclear whether the proposed muscle synergy extrapolation method based on non-negative matrix factorisation is sufficiently sensitive to capture spasticity provoked EMG activations commonly used to inform clinical decision making in children with CP 41 . Although recent study 42 found synergy extrapolation method promising, it is yet to explore how neuromusculoskeletal model would perform when synergies extracted from two groups of population (healthy and impaired) are combined and used as input. Future use of the proposed synergy extrapolation method could be combined with neuromusculoskeletal models to predict internal biomechanics in children with CP.…”

How to Decide the Number of Gait Cycles in Different Low-Pass Filters to Extract Motor Modules by Non-negative Matrix Factorization During Walking in Chronic Post-stroke Patients

“…Finally, the influence of pre-processing, except for LPFs, has not been studied in this study. Previous studies suggested that different band-pass filters, EMG normalization methods, and output normalization methods slightly influenced muscle weightings ( Banks et al, 2017 ; Shuman et al, 2017 ; Kieliba et al, 2018 ; Ao et al, 2020 ). In particular, Shuman et al (2017) suggested that muscle weightings were more similar in different LPF conditions for EMG data normalized to unit variance than peak amplitude in children.…”

EMG-driven musculoskeletal model calibration with estimation of unmeasured muscle excitations via synergy extrapolation

“…While calibration of activation dynamics and muscle-tendon model parameter values is built into the EMG-driven modeling process, several practical challenges exist with collecting EMG data from all muscles that contribute significantly to a specified movement task ( Sartori et al, 2014 ; Péter et al, 2019 ; Zonnino and Sergi, 2019 ; Ao et al, 2020 ; Gurchiek et al, 2020 ). First, surface electrodes, which are non-invasive and easily applied, cannot measure EMG signals from deep muscles that contribute significantly to joint moments.…”

“…More recently, the muscle synergy concept has been investigated for estimating muscle activations via SO or muscle excitations via EMG-driven modeling ( Bianco et al, 2018 ; Ao et al, 2020 ; Michaud et al, 2020 ; Shourijeh and Fregly, 2020 ). A muscle synergy consists of a time-varying synergy excitation (or activation) and a corresponding time-invariant synergy vector containing weights that define how the synergy excitation (or activation) contributes to the excitation (or activation) of all muscles ( Cappellini et al, 2006 ; Tresch et al, 2006 ; Ting and Chvatal, 2010 ; Banks et al, 2017 ).…”