Muscle Synergies May Improve Optimization Prediction of Knee Contact Forces During Walking

Abstract

The ability to predict patient-specific joint contact and muscle forces accurately could improve the treatment of walking-related disorders. Muscle synergy analysis, which decomposes a large number of muscle electromyographic (EMG) signals into a small number of synergy control signals, could reduce the dimensionality and thus redundancy of the muscle and contact force prediction process. This study investigated whether use of subject-specific synergy controls can improve optimization prediction of knee contact forces during walking. To generate the predictions, we performed mixed dynamic muscle force optimizations (i.e., inverse skeletal dynamics with forward muscle activation and contraction dynamics) using data collected from a subject implanted with a forcemeasuring knee replacement. Twelve optimization problems (three cases with four subcases each) that minimized the sum of squares of muscle excitations were formulated to investigate how synergy controls affect knee contact force predictions. The three cases were: (1) CalibrateþMatch where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously matched, (2) Precalibrateþ Predict where experimental knee contact forces were predicted using precalibrated muscle model parameters values from the first case, and (3) Calibrateþ Predict where muscle model parameter values were calibrated and experimental knee contact forces were simultaneously predicted, all while matching inverse dynamic loads at the hip, knee, and ankle. The four subcases used either 44 independent controls or five synergy controls with and without EMG shape tracking. For the CalibrateþMatch case, all four subcases closely reproduced the measured medial and lateral knee contact forces (R 2 ! 0.94, root-mean-square (RMS) error < 66 N), indicating sufficient model fidelity for contact force prediction. For the PrecalibrateþPredict and CalibrateþPredict cases, synergy controls yielded better contact force predictions (0.61 < R 2 < 0.90, 83 N < RMS error < 161 N) than did independent controls (-0.15 < R 2 < 0.79, 124 N < RMS error < 343 N) for corresponding subcases. For independent controls, contact force predictions improved when precalibrated model parameter values or EMG shape tracking was used. For synergy controls, contact force predictions were relatively insensitive to how model parameter values were calibrated, while EMG shape tracking made lateral (but not medial) contact force predictions worse. For the subject and optimization cost function analyzed in this study, use of subject-specific synergy controls improved the accuracy of knee contact force predictions, especially for lateral contact force when EMG shape tracking was omitted, and reduced prediction sensitivity to uncertainties in muscle model parameter values.

Analysis of muscle synergies and activation–deactivation patterns in subjects with anterior cruciate ligament deficiency during walking

“…Some authors used the muscle synergy components to decrease the indeterminacy in the muscle force calculations, either in forward dynamics (Allen and Neptune, 2012;Neptune et al, 2009) or inverse dynamics (Walter et al, 2014) approaches. Regarding subjects with ACL deficiency, differences have been observed at the joint level as well as at individual EMG signals (Houck et al, 2007;Knoll et al, 2004;Rudolph et al, 2001;Serrancoli et al, 2014).…”

“…Recently, some studies used muscle synergy components to decrease the indeterminacy when calculating the muscle forces in dynamic simulations of healthy subjects (Allen and Neptune, 2012;Neptune et al, 2009;Walter et al, 2014). Using muscle synergy components extracted from subjects with ACL deficiency could be also useful to predict muscle forces in gait dynamic analyses of those subjects.…”

Neuromusculoskeletal Model Calibration Significantly Affects Predicted Knee Contact Forces for Walking

“…Recent work has also shown that neural commands extracted from a subset of experimentally measured EMG signals can reconstruct the shapes of the omitted EMG signals accurately [21]. Thus, it may be physiologically reasonable to use experimentally derived neural commands as basis functions to construct all model-predicted muscle activations [22]. In addition, instrumented knee studies performed using force-measuring knee replacements have provided internal force data that permit at least two additional inverse dynamic knee loads to be used as constraints in the muscle force estimation and model calibration process [23,24].…”

Musculoskeletal Simulation for Assessment of Effect of Movement-Based Structure-Modifying Treatment Strategies

“…The challenges related to these gait-retaining strategies remain in the determination of optimal strategy and magnitude of modifications required to maximize beneficial effects and an efficient and special training method with real-time biofeedback is needed [14]. Muscle synergies analysis based on the dimensional reduction principle (i.e., matrix factorization) allows studying how the nervous system reduces movement control complexity [40]. In this present study, the muscle synergy analysis provides muscle activation pattern of each specific muscle related to each gait modification (bouncy, medial thrust, mild crouch, and mtp) leading to determining its effect on the muscle biomechanics.…”