Characterizing Parkinson´s disease from gait dynamics using chaos descriptors

David Ricaurte; Gustavo Pineda Ortiz; Eduardo Romero Castro M.D.

doi:10.1117/12.2606313

10 December 2021 Characterizing Parkinson´s disease from gait dynamics using chaos descriptors

David Ricaurte, Gustavo Pineda Ortiz, Eduardo Romero Castro M.D.

Proceedings Volume 12088, 17th International Symposium on Medical Information Processing and Analysis; 1208805 (2021) https://doi.org/10.1117/12.2606313
Event: Seventeenth International Symposium on Medical Information Processing and Analysis, 2021, Campinas, Brazil

Abstract

Motor disorders in Parkinson’s Disease (PD) show high inter-individual variability which challenges the current observational-based strategies in the clinical setting to determine the actual disease evolution and monitoring the therapy response. In spite the recent development of the motion capture technology, it is still hardly transferable to the routine exam and the non-linear disease patterns are poorly explored. Because gait pattern could be approached as deterministic chaotic system, this work aimed to non-linearly represent lower limb kinematic standing out the differences among PD stages. For doing so, a widely used deep learning framework was implemented for obtaining the body landmarks and their temporal series and thereafter, construing the phase space based on the first order derivatives. Largest Lyapunov exponent, correlation dimension and approximate entropy were then computed resulting in statistically significant differences (Wilcoxon rank test, p < 0.05), particularly between healthy controls and stages 3, the most advanced stage, and comparing stage 1 face to stage 3. These finding providing insights how the complex patterns may be related with the disease progression in PD and easily implemented using affordable video devices.

Citation Download Citation

David Ricaurte, Gustavo Pineda Ortiz, and Eduardo Romero Castro M.D. "Characterizing Parkinson´s disease from gait dynamics using chaos descriptors", Proc. SPIE 12088, 17th International Symposium on Medical Information Processing and Analysis, 1208805 (10 December 2021); https://doi.org/10.1117/12.2606313

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KEYWORDS

Gait analysis

Control systems

Kinematics

Chaos

Video

Complex systems

Statistical analysis

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