Electromyography-informed modeling for estimating muscle activation and force alterations in Parkinson’s disease

Abstract

Electromyography (EMG)-driven neuromusculoskeletal modeling (NMSM) enables simulating the mechanical function of multiple muscle-tendon units as controlled by nervous system in the generation of complex movements. In the context of clinical assessment this may enable understanding biomechanical factor contributing to gait disorders such as one induced by Parkinson’s disease (PD). In spite of the challenges in the development of patient-specific models, this preliminary study aimed at establishing a feasible and noninvasive experimental and modeling pipeline to be adopted in clinics to detect PD-induced gait alterations. Four different NMSM have been implemented for three healthy controls using CEINMS, an OpenSim-compatible toolbox. Models differed in the EMG-normalization methods used for calibration purposes (i.e. walking trial normalization and maximum voluntary contraction normalization) and in the set of experimental EMGs used for the musculotendon-unit mapping (i.e. 4 channels vs. 15 channels). Model accuracy assessment showed no statistically significant differences between the more complete model (non-clinically viable) and the proposed reduced one (clinically viable). The clinically viable reduced model was systematically applied on a dataset including ten PD’s and thirteen healthy controls. Results showed significant differences in the neuromuscular control strategy of the PD group in term of muscle forces and joint torques. Indeed, PD patients displayed a significantly lower magnitude on force production and revealed a higher amount of force variability with the respect of the healthy controls. The estimated variables could become a measurable biomechanical outcome to assess and track both disease progression and its impact on gait in PD subjects.

Keywords:

• Parkinson’s disease
• neuromusculoskeletal modeling
• EMG-informed approach
• gait analysis
• neuromuscular disorders
• rehabilitation

Acknowledgments

Marco Romanato wishes to acknowledge Fondazione Fresco Parkinson Institute Italia Onlus for the financial contribution to his PhD course. The part of this work performed by Annamaria Guiotto, was supported by MIUR (Italian Minister for Education) under the initiative ‘Departments of Excellence’ (Law 232/2016).

Disclosure statement

The authors declare no conflict of interest.