A model-based approach to predict muscle synergies using optimization: application to feedback control

Razavian, Reza Sharif; Mehrabi, Naser; McPhee, John

doi:10.3389/fncom.2015.00121

Cited by 29 publications

(19 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To bridge this gap while also addressing the EMG normalization issue, we perform muscle synergy analysis for our subject within the larger context of producing a dynamically consistent full-body walking motion using a subject-specific neuromusculoskeletal model (Sharif Razavian et al, 2015 ). With standard synergy analysis, the goal is to find a specified number of synergy controls and vectors that best match a larger set of normalized muscle EMG signals.…”

Section: Methodsmentioning

confidence: 99%

Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions

Meyer

Eskinazi

Jackson

et al. 2016

Front. Bioeng. Biotechnol.

131

View full text Add to dashboard Cite

Researchers have explored a variety of neurorehabilitation approaches to restore normal walking function following a stroke. However, there is currently no objective means for prescribing and implementing treatments that are likely to maximize recovery of walking function for any particular patient. As a first step toward optimizing neurorehabilitation effectiveness, this study develops and evaluates a patient-specific synergy-controlled neuromusculoskeletal simulation framework that can predict walking motions for an individual post-stroke. The main question we addressed was whether driving a subject-specific neuromusculoskeletal model with muscle synergy controls (5 per leg) facilitates generation of accurate walking predictions compared to a model driven by muscle activation controls (35 per leg) or joint torque controls (5 per leg). To explore this question, we developed a subject-specific neuromusculoskeletal model of a single high-functioning hemiparetic subject using instrumented treadmill walking data collected at the subject’s self-selected speed of 0.5 m/s. The model included subject-specific representations of lower-body kinematic structure, foot–ground contact behavior, electromyography-driven muscle force generation, and neural control limitations and remaining capabilities. Using direct collocation optimal control and the subject-specific model, we evaluated the ability of the three control approaches to predict the subject’s walking kinematics and kinetics at two speeds (0.5 and 0.8 m/s) for which experimental data were available from the subject. We also evaluated whether synergy controls could predict a physically realistic gait period at one speed (1.1 m/s) for which no experimental data were available. All three control approaches predicted the subject’s walking kinematics and kinetics (including ground reaction forces) well for the model calibration speed of 0.5 m/s. However, only activation and synergy controls could predict the subject’s walking kinematics and kinetics well for the faster non-calibration speed of 0.8 m/s, with synergy controls predicting the new gait period the most accurately. When used to predict how the subject would walk at 1.1 m/s, synergy controls predicted a gait period close to that estimated from the linear relationship between gait speed and stride length. These findings suggest that our neuromusculoskeletal simulation framework may be able to bridge the gap between patient-specific muscle synergy information and resulting functional capabilities and limitations.

show abstract

Section: Methodsmentioning

confidence: 99%

Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions

Meyer

Eskinazi

Jackson

et al. 2016

Front. Bioeng. Biotechnol.

131

View full text Add to dashboard Cite

show abstract

“…NMF instead returns always positive synergies compositions and activations, being non-negative by nature. Furthermore, it is a reliable method that reflects the features of EMG signals (d’Avella et al, 2008 ; Razavian et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Muscle Synergies-Based Characterization and Clustering of Poststroke Patients in Reaching Movements

Scano

Chiavenna

Malosio

et al. 2017

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

BackgroundA deep characterization of neurological patients is a crucial step for a detailed knowledge of the pathology and maximal exploitation and customization of the rehabilitation therapy. The muscle synergies analysis was designed to investigate how muscles coactivate and how their eliciting commands change in time during movement production. Few studies investigated the value of muscle synergies for the characterization of neurological patients before rehabilitation therapies. In this article, the synergy analysis was used to characterize a group of chronic poststroke hemiplegic patients.MethodsTwenty-two poststroke patients performed a session composed of a sequence of 3D reaching movements. They were assessed through an instrumental assessment, by recording kinematics and electromyography to extract muscle synergies and their activation commands. Patients’ motor synergies were grouped by the means of cluster analysis. Consistency and characterization of each cluster was assessed and clinically profiled by comparison with standard motor assessments.ResultsMotor synergies were successfully extracted on all 22 patients. Five basic clusters were identified as a trade-off between clustering precision and synthesis power, representing: healthy-like activations, two shoulder compensatory strategies, two elbow predominance patterns. Each cluster was provided with a deep characterization and correlation with clinical scales, range of motion, and smoothness.ConclusionThe clustering of muscle synergies enabled a pretherapy characterization of patients. Such technique may affect several aspects of the therapy: prediction of outcomes, evaluation of the treatments, customization of doses, and therapies.

show abstract

“…Furthermore, the COM can be further simplified using muscle synergy theory, in which the CNS coordinates human body movements by bundling individual muscles into groups. This allows a low-dimensional control input that significantly reduces of the size of the control problem (Sharif Razavian et al, 2015). …”

Section: Discussionmentioning

confidence: 99%

Predictive Simulation of Reaching Moving Targets Using Nonlinear Model Predictive Control

Mehrabi

Razavian

Ghannadi

et al. 2017

Front. Comput. Neurosci.

Self Cite

View full text Add to dashboard Cite

This article investigates the application of optimal feedback control to trajectory planning in voluntary human arm movements. A nonlinear model predictive controller (NMPC) with a finite prediction horizon was used as the optimal feedback controller to predict the hand trajectory planning and execution of planar reaching tasks. The NMPC is completely predictive, and motion tracking or electromyography data are not required to obtain the limb trajectories. To present this concept, a two degree of freedom musculoskeletal planar arm model actuated by three pairs of antagonist muscles was used to simulate the human arm dynamics. This study is based on the assumption that the nervous system minimizes the muscular effort during goal-directed movements. The effects of prediction horizon length on the trajectory, velocity profile, and muscle activities of a reaching task are presented. The NMPC predictions of the hand trajectory to reach fixed and moving targets are in good agreement with the trajectories found by dynamic optimization and those from experiments. However, the hand velocity and muscle activations predicted by NMPC did not agree as well with experiments or with those found from dynamic optimization.

show abstract

A model-based approach to predict muscle synergies using optimization: application to feedback control

Cited by 29 publications

References 47 publications

Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions

Muscle Synergies Facilitate Computational Prediction of Subject-Specific Walking Motions

Muscle Synergies-Based Characterization and Clustering of Poststroke Patients in Reaching Movements

Predictive Simulation of Reaching Moving Targets Using Nonlinear Model Predictive Control

Contact Info

Product

Resources

About