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Quantifying Viscous Damping and Stiffness in Parkinsonism Using Data-Driven Model Estimation

Rigidity of upper and lower limbs in Parkinson’s disease (PD) is typically assessed via a clinical rating scale that is subject to biases inherent to human perception. Methodologies and systems to systematically quantify changes in rigidity associated with angular position (stiffness) or velocity (viscous damping) are needed to enhance our understanding of PD pathophysiology and objectively assess therapies. We developed a robotic system and a model-based approach to estimate viscous damping and stiffness of the elbow. Our methodology enables the subject to freely rotate the elbow, while torque perturbations tailored to identify the arm dynamics are delivered. The viscosity and stiffness are calculated based on the experimental data using least-squares optimization. We validated our technique using computer simulations of the arm dynamics and experiments with a nonhuman animal model of PD in the presence and absence of deep brain stimulation (DBS) therapy. Computer simulations of the arm demonstrate that the proposed approach can accurately estimate viscous damping and stiffness. The experimental data show that both viscosity and stiffness significantly decreased when DBS was delivered. Computer simulations and experiments showed that stiffness and viscosity measurements obtained with the proposed methodology could better differentiate changes in rigidity than scores previously used for research, including the work score, impulse score, and modified Unified Parkinson’s Disease Rating Scale (mUPDRS). The proposed estimation method is suitable to quantify the effect of therapies on viscous damping and stiffness and study the pathophysiological mechanisms underlying rigidity in PD.