SYNERGY ONE LENDING SACRAMENTO CA FULL

Knowledge of muscle forces could provide valuable insight into not only the neural control strategies employed by the central nervous system (CNS) (Contessa and Luca, 2013 Del Vecchio et al., 2018) but also 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). SynX may facilitate the assessment of human neuromuscular control and biomechanics when important EMG signals are missing. 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. 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. SynX performance was most influenced by the choice of synergy analysis algorithm and number of synergies. 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. 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. One fine wire EMG channel (either iliopsoas or adductor longus) was then treated as unmeasured.

SYNERGY ONE LENDING SACRAMENTO CA FULL

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. 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. 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). 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 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. However, a critical challenge for practical deployment of this approach is missing EMG data from muscles that contribute substantially to joint moments. Electromyography (EMG)-driven musculoskeletal modeling relies on high-quality measurements of muscle electrical activity to estimate muscle forces.

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