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.
Highlights
Across all EMG normalization methods, maximum value over all trials (MaxOver) produced significantly higher Variance accounted for (VAF) values than did variance over all trials (VarOver) (p < 0.01), variance per trial (VarPer) (p = 0.028), and magnitude per trial (MagPer) (p < 0.01), while maximum value per trial (MaxPer) produced the lowest VAF amongst the five EMG normalization methods (p < 0.01)
This study showed that SynX is a viable option for estimating an unmeasured muscle excitation using synergy excitations extracted from measured muscle excitations
The study demonstrated that methodological choices made before MSA affect the accuracy with which unmeasured muscle excitations can be predicted
Summary
Knowledge of muscle forces could provide valuable insight into the neural control strategies employed by the central nervous system (CNS) (Contessa and Luca, 2013; Del Vecchio et al, 2018) and the development of effective treatments for neuromusculoskeletal disorders (Shao et al, 2009; Fregly et al, 2012b, Fregly et al, 2012a; Allen et al, 2013; Pitto et al, 2019; Sauder et al, 2019). Since direct measurement of muscle force is generally not possible, computational techniques have been developed to generate muscle force estimates (Anderson and Pandy, 2001; Lloyd and Besier, 2003; Thelen et al, 2003; Buchanan et al, 2005; Shao et al, 2009). EMG-driven musculoskeletal modeling is a computational approach for predicting muscle forces that can bypass the muscle redundancy problem while simultaneously allowing for calibration of musculotendon properties (e.g., optimal muscle fiber length; Lloyd and Besier, 2003; Amarantini and Martin, 2004; Shao et al, 2009; Sartori et al, 2012; Meyer et al, 2017). Nonlinear optimization is used to calibrate musculotendon model parameters such that predicted net joint moments match inverse dynamic joint moments as closely as possible
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