Synergy Feedback Control Predicts Walking Across Multiple Cycles

Neural feedback is important for healthy control of movement, and multiple neurological disorders (e.g., stroke, cerebral palsy, Parkinson's disease, incomplete spinal cord injury) can be described by how they impair healthy feedback or induce unhealthy feedback. Researchers have created numerous computational neuromusculoskeletal models controlled by simulated neural feedback mechanisms, but these models rarely represent actual human subjects and thus have not found practical application in treating patients with movement impairments. As a step toward designing patient-specific treatments for individuals with neurological disorders, this study used the Neuromusculoskeletal Modeling Pipeline to develop and evaluate a novel synergy-based feedforward (FF)+feedback (FB) model using a personalized, three-dimensional neuromusculoskeletal walking model of an actual human subject post-stroke. Experimental walking data collected from the subject were used to create the subject's personalized walking model. This model was used to calculate lower body muscle activations consistent with the subject's electromyographic, joint motion, and ground reaction data for 5 calibration walking cycles. Nominal FF synergy controls were calculated by averaging the muscle synergies that closely reconstructed the 5 cycles of muscle activations and associated joint moments simultaneously. These nominal FF controls were then scaled by 0, 25, 50, 75, 100, and 125%, and the gap in reproducing individual cycle muscle activations was filled by fitting FB synergy controls as a function of joint positions, velocities, and moments as surrogates for muscle lengths, muscle velocities, and tendon forces. Finally, the six synergy-based FF+FB models controlled the subject's personalized walking model in predictive simulations performed for 3 testing walking cycles withheld from calibration. The 100% FF model (which still had minimal FB) reproduced the testing walking cycles the most closely, and only the 75%, 100%, and 125% FF models generated near-periodic walking motions using initial conditions consistent with experimental values. The 0, 25, and 50% FF models could generate near-periodic walking motions only when the initial conditions were allowed to diverge substantially from experimental values. Our findings suggest that predictive simulations of walking using real experimental data may require a minimum level of feedforward control and sufficient fitting data to predict a subject's actual dynamically consistent motion.