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Neural modeling of human motor coordination inspired by biological signals aiming for parkinsonian gaits

Andrii Dmytrovych Shachykov 1, 2 
1 NEUROSYS - Analysis and modeling of neural systems by a system neuroscience approach
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : My thesis aims to simulate the impact of motor disorders on the human gait to help non-invasive diagnosis of neurodegenerative diseases such as Parkinson's disease. Indeed, the simulation of the human locomotor system helps to deepen our understanding of the functioning of the human body by providing biological, biomechanical and kinematic data that would be difficult to collect otherwise and by helping to evaluate the coordination of a patient's movements to predict its condition after surgery. The goal of my thesis is, more specifically, to create a new platform for neuro-musculoskeletal simulation of the human locomotor system to reproduce healthy or altered walking gaits by Parkinson's disease or by disorders of the musculoskeletal system or locomotor disorders. The work presented includes several matters. Firstly, the main principles of the nervous system that control human locomotion are reviewed, by focusing on neural structures located in the brain and which are the sources of parkinsonian disorders. The neural controller of the simulation platform is based on an original model of central pattern generator (CPG) inspired by the spinal locomotor network and developed at LORIA in recent years. The musculoskeletal simulators are used in this thesis to obtain a closed-loop physical simulation of the locomotor system walking on the ground and whose proprioceptive and exteroceptive sensory feedback is used by the CPGs. The musculoskeletal simulator GAIT2DE was used with the OpenSim simulator which is more realistic and more used in Biomechanics field. The simulated gait analysis and controller parameter optimization are concerned followed by the results obtained with the simulators. These results show that it is possible to generate different walking patterns that are relatively stable and coordinated by modifying the neuronal parameters of GPCs. The simulation platform will allow to simulate abnormal gait due to different causes such as neurodegenerative diseases or the impact of the addition of artificial limbs (prostheses) and surgical interventions.
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Submitted on : Tuesday, June 2, 2020 - 3:31:03 PM
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  • HAL Id : tel-02735309, version 1


Andrii Dmytrovych Shachykov. Neural modeling of human motor coordination inspired by biological signals aiming for parkinsonian gaits. Computer Science [cs]. Université de Lorraine; Institut polytechnique de Kiev (Ukraine), 2019. English. ⟨NNT : 2019LORR0291⟩. ⟨tel-02735309⟩



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