Title: “Neural modeling of human motor coordination inspired by biological signals aiming for parkinsonian gaits”
The members of the jury:
- M. Patrick HENAFFLORIA – CNRS, INRIA et Université de Lorraine – Co-directeur de thèse
- M. Oleksandr SHULYAKNTUU ”Igor Sikorsky Kyiv Polytechnic Institute” – Co-directeur de thèse
- Mme Tetiana AKSENOVACEA Grenoble – Rapporteur
- M. Yannick AOUSTIN Université de Nantes – Rapporteur
- Mme Christine AZEVEDO Laboratoire d’Informatique et de Robotique de Montpellier – Examinateur
- M. Julien FRERE Université de Lorraine – Examinateur
- M. Anton POPOVNTUU ”Igor Sikorsky Kyiv Polytechnic Institute” – Examinateur
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.
For that, 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. Next, I describe how control signals are transmitted in the spinal cord to control muscle activity through several closed loops.
Then, the neural control of the future simulation platform is presented. This controller is based on an original model of central pattern generator (CPG) inspired by the spinal locomotor network and developed at LORIA in recent years. This CPG model can generate variable rhythmic signals according to its intrinsic neural parameters, which are controlled by downlink signals from the decision-making module modeling the behavior of the basal ganglia. CPG motoneuron output signals are applied as an excitation to the flexor / extensor muscles in the model.
After that, I describe the musculoskeletal simulators used in this thesis as well as the modifications made 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. As a first step, the musculoskeletal simulator GAIT2DE was used for simplicity, then the method and models were implemented in the OpenSim simulator which is more realistic and more used in Biomechanics field.
Further, the simulated gait analysis and controller parameter optimization are considered. The gait analysis part consists of gait cycle explanation and its application for decomposing of simulated and real human gait data. The gait cycle is used in comparison of simulation data with parameters of real gait and controller optimization method based on comparative analysis using cross-correlation.
Final part of thesis presents the results obtained with the OpenSim and GAIT2DE simulators by integrating a complete circuitry based on CPGs and a reflex controller of equilibrium based on proprioceptive feedback. 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, thus reproducing the patterns observed for Parkinson’s disease or other well-known gait disorders in medicine. Finally, I show that this platform can simulate various paces due to Parkinson’s disease or muscle degeneration.
The last part of work concerns the conclusion and the perspectives. It summarizes the work developed in the thesis by proposing some improvements in CPG circuitry and in the modifications that should be made to OpenSim. The perspectives concern the simulation platform as a whole that 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.