Modular neuromuscular control of human locomotion by central pattern generator |
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| Affiliation: | 1. Mechanical Engineering Department, Sharif University of Technology, Azadi Avenue, Tehran, Iran;2. RCBTR, Tehran University of Medical Sciences, Tehran, Iran;1. Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Knoxville, TN, USA;2. Bioengineering, Stanford University, Stanford, CA, USA;1. Department of Motor Science and Wellness, University of Naples Parthenope, Via Medina 40, Naples, Italy;2. Department of Neurology, Second University of Naples, Via Costantinopoli 104, Naples, Italy;3. Geriatric Unit Frullone ASL NA1, Via Comunale del Principe, 16/A, Naples, Italy;4. Institute for Diagnosis and Cure Hermitage Capodimonte, Via Cupa delle Tozzole 2, Naples, Italy;5. Department of Engineering, University of Naples Parthenope, Centro Direzionale Isola C4, Naples, Italy;1. Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, IL, USA;2. Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA;3. Department of Kinesiology, Michigan State University, East Lansing, MI, USA;4. Department of Physical Medicine and Rehabilitation, University of Michigan, Ann Arbor, MI, USA;1. Department of Mechanical Engineering, Cleveland State University, USA;2. Biorobotics Laboratory, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland;3. Machine Learning and Data Analytics Lab, Faculty of Engineering, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany;1. Department of Physiotherapy, Bildungszentrum Gesundheit Basel-Stadt, 4142, Muenchenstein, Switzerland;2. CAPHRI School for Public Health and Primary Care, Maastricht University, 6200 MD, Maastricht, The Netherlands;3. Department of Anatomy and Embryology, Maastricht University, 6200 MD, Maastricht, The Netherlands;4. Department of Epidemiology, Maastricht University, 6200 MD, Maastricht, The Netherlands |
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| Abstract: | The central pattern generators (CPG) in the spinal cord are thought to be responsible for producing the rhythmic motor patterns during rhythmic activities. For locomotor tasks, this involves much complexity, due to a redundant system of muscle actuators with a large number of highly nonlinear muscles. This study proposes a reduced neural control strategy for the CPG, based on modular organization of the co-active muscles, i.e., muscle synergies. Four synergies were extracted from the EMG data of the major leg muscles of two subjects, during two gait trials each, using non-negative matrix factorization algorithm. A Matsuoka׳s four-neuron CPG model with mutual inhibition, was utilized to generate the rhythmic activation patterns of the muscle synergies, using the hip flexion angle and foot contact force information from the sensory afferents as inputs. The model parameters were tuned using the experimental data of one gait trial, which resulted in a good fitting accuracy (RMSEs between 0.0491 and 0.1399) between the simulation and experimental synergy activations. The model׳s performance was then assessed by comparing its predictions for the activation patterns of the individual leg muscles during locomotion with the relevant EMG data. Results indicated that the characteristic features of the complex activation patterns of the muscles were well reproduced by the model for different gait trials and subjects. In general, the CPG- and muscle synergy-based model was promising in view of its simple architecture, yet extensive potentials for neuromuscular control, e.g., resolving redundancies, distributed and fast control, and modulation of locomotion by simple control signals. |
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| Keywords: | Rhythmic activity Motor program Motor pattern Muscle redundancy Muscle synergies |
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