Biology subjects

Fregly, B. J.

Publications and source records attributed to Fregly, B. J..

4 recordsLinked to original sources

bioRxiv2020

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

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 electrodes. Muscle synergy analysis is a dimensionality-reduction approach to decompose 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 corresponding synergy excitation to a specific muscle excitation. This study evaluates how accurately missing muscle excitations can be predicted using synergy excitations extracted from muscles with available EMGs (henceforth called "synergy extrapolation"). The results were reported on a gait dataset 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-channel EMGs (including surface and indwelling) in each leg. One indwelling EMG (either iliopsoas or adductor longus) was then treated as unmeasured at a time. The synergy weights associated with the unmeasured muscle were predicted through solving a nonlinear optimization problem where the errors between inverse dynamics and EMG-driven joint moments were minimized. We also quantitatively evaluated how synergy analysis algorithms (principal component analysis (PCA) and non-negative matrix factorization (NMF)), EMG normalization methods, and number of synergies affect the accuracy of the predicted unmeasured muscle excitation. Synergy extrapolation performance was most influenced by the choice of synergy analysis algorithm and number of synergies. PCA with 5 or 6 synergies consistently predicted unmeasured muscle excitations most accurately and with greatest robustness to choice of EMG normalization method. Furthermore, the associated joint moment matching accuracy was comparable to that produced by the full EMG-driven calibration. The synergy extrapolation method described in this study may facilitate the assessment of human neuromuscular control and biomechanics in response to surgical or rehabilitation treatment when important EMG signals are missing.

bioengineering↗

bioRxiv2020

Musculoskeletal Model Personalization Affects Metabolic Cost Estimates for Walking

Assessment of metabolic energy cost as a metric for human performance has expanded across various fields within the scientific, clinical, and engineering communities. As an alternative to measuring metabolic cost experimentally, musculoskeletal models incorporating metabolic cost models have been developed. However, to utilize these models for practical applications, the accuracy of their metabolic cost predictions requires improvement. Previous studies have reported the benefits of using personalized musculoskeletal models for various applications, yet no study has evaluated how model personalization affects metabolic cost estimation. This study investigated the effect of musculoskeletal model personalization on estimates of metabolic cost of transport (CoT) during post-stroke walking using three commonly used metabolic cost models. We analyzed data previously collected from two male stroke survivors with right-sided hemiparesis. The three metabolic cost models were implemented within three musculoskeletal modeling approaches involving different levels of personalization. The first approach used a scaled generic OpenSim model and found muscle activations via static optimization (SOGen). The second approach used a personalized EMG-driven musculoskeletal model with personalized functional axes but found muscle activations via static optimization (SOCal). The third approach used the same personalized EMG-driven model but calculated muscle activations directly from EMG data (EMGCal). For each approach, the muscle activation estimates were used to calculate each subjects cost of transport (CoT) at different gait speeds using three metabolic cost models (Umberger 2003, Umberger 2010, and Bhargava 2004). The calculated CoT values were compared with published CoT trends as a function of stance time, double support time, step positions, walking speed, and severity of motor impairment (i.e., Fugl-Meyer score). Overall, U10-SOCal, U10-EMGCal, U03-SOCal, and U03-EMGCal were able to produce slopes between CoT and the different measures of walking asymmetry that were statistically similar to those found in the literature. Although model personalization seemed to improve CoT estimates, further tuning of parameters associated with the different metabolic cost models in future studies may allow for realistic CoT predictions. An improvement in CoT predictions may allow researchers to predict human performance, surgical, and rehabilitation outcomes reliably using computational simulations.

bioengineering↗

bioRxiv2020

How Well Do Commonly Used Co-Contraction Indices Approximate Lower Limb Joint Stiffness Trends during Gait?

Muscle co-contraction generates joint stiffness to improve stability and accuracy during limb movement but at the expense of higher energetic cost. The quantification of joint stiffness generated from muscle co-contraction is difficult through both experimental and computational means for its benefit and cost to be assessed. Quantification of muscle co-contraction may offer an alternative path for estimating joint stiffness. By choosing the commonly used Co-Contraction Indices (CCIs) to represent muscle co-contraction, this study investigated the feasibility of using CCI to approximate lower limb joint stiffness trends during gait. A calibrated EMG-driven musculoskeletal model of a hemiparetic individual post-stroke from a previous study was used to generate the quantities required for CCI calculation and model-based estimation of joint stiffness. A total of 14 classes of CCIs for various combinations of antagonistic muscle pairs were calculated based on two common CCI formulations, each with 7 types of quantities that included variations of electromyography (EMG) signals and joint moments from the muscles. Correlations between CCIs and model-based estimates of sagittal plane stiffness of the lower extremity joints (hip, knee, ankle) were computed. Although moderate to strong correlation was observed between some CCI formulations and the corresponding joint stiffness, these associations were highly dependent on the methodological choices made for CCI computation. The overall findings of this study were the following: (1) the formulation proposed by Rudolph et al. (2000), CCI1, was more correlated with joint stiffness than that of Falconer and Winter (1985); (2) Moment-based CCI1 from individual antagonistic muscle pairs was more correlated than EMG-based CCI1; (3) EMG signals with calibrated electromechanical delay and joint moment generated by individual muscle without normalization to a reference value were the most correlated for EMG-based CCI1 and moment-based CCI1, respectively. The combination of antagonistic muscle pairs for most correlated within each CCI class was also identified. By using CCI to approximate joint stiffness trends, this study may open an alternative path to studying joint stiffness.

bioengineering↗

bioRxiv2019

DO MUSCLE SYNERGIES IMPROVE OPTIMIZATION PREDICTION OF MUSCLE ACTIVATIONS DURING GAIT?

Determination of muscle forces during motion can help to understand motor control, assess pathological movement, diagnose neuromuscular disorders, or estimate joint loads. Difficulty of in vivo measurement made computational analysis become a common alternative in which, as several muscles serve each degree of freedom, the muscle redundancy problem must be solved. Unlike static optimization (SO), synergy optimization (SynO) couples muscle activations across all time frames, thereby altering estimated muscle co-contraction. This study explores whether the use of a muscle synergy structure within a static optimization framework improves prediction of muscle activations during walking. A motion/force/EMG gait analysis was performed on five healthy subjects. A musculoskeletal model of the right leg actuated by 43 Hill-type muscles was scaled to each subject and used to calculate joint moments, muscle-tendon kinematics and moment arms. Muscle activations were then estimated using SynO with two to six synergies and traditional SO, and these estimates were compared with EMG measurements. SynO neither improved SO prediction of experimental activation patterns nor provided SO exact matching of joint moments. Finally, synergy analysis was performed on SO estimated activations, being found that the reconstructed activations produced poor matching of experimental activations and joint moments. As conclusion, it can be said that, although SynO did not improve prediction of muscle activations during gait, its reduced dimensional control space could be beneficial for applications such as functional electrical stimulation (FES) or motion control and prediction.

neuroscience↗