Simulation Of Parkinsonian Gait By Fusing Trunk Learned Patterns And A Lower Limb First Order Model

Parkinson's disease is a neurodegenerative disorder that progressively affects the movement. Gait analysis is therefore crucial to determine a disease degree as well as to orient the diagnosis. However, gait examination is completely subjective and therefore prone to errors or misinterpretations, even with a great expertise. In addition, the conventional evaluation follows up general gait variables, which amounts to ignore subtle changes that definitely can modify the history of the treatment. This work presents a functional gait model that simulates the center of gravity trajectory (CoG) for different parkinson disease stages. This model mimics the gait trajectory by coupling two models: a double pendulum (single stance phase) and a spring-mass model (double stance). Realistic simulations for different parkinson disease stages are then obtained by integrating to the model a set of trunk bending patterns, learned from real patients. The proposed model was compared with the CoG of real parkinson gaits in stages 2, 3, 4 achieving a correlation coefficient of 0.88, 0.92 and 0.86, respectively.