28 January 2015 Simulation of Parkinsonian gait by fusing trunk learned patterns and a lower limb first order model
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Proceedings Volume 9287, 10th International Symposium on Medical Information Processing and Analysis; 92871B (2015) https://doi.org/10.1117/12.2073381
Event: Tenth International Symposium on Medical Information Processing and Analysis, 2014, Cartagena de Indias, Colombia
Abstract
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.
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Luisa Cárdenas, Luisa Cárdenas, Fabio Martínez, Fabio Martínez, Eduardo Romero, Eduardo Romero, "Simulation of Parkinsonian gait by fusing trunk learned patterns and a lower limb first order model", Proc. SPIE 9287, 10th International Symposium on Medical Information Processing and Analysis, 92871B (28 January 2015); doi: 10.1117/12.2073381; https://doi.org/10.1117/12.2073381
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