Session: 08-24-01: Human-Machine Interaction: Design, Dynamics, and Control

Paper Number: 166780

Biomechanics and Neuromuscular Models for Parkinsonian Gait and Tremor 

Understanding the complex interplay between neuromuscular dynamics and biomechanical motion is critical for advancing diagnostic and therapeutic strategies for movement disorders such as Parkinson’s disease. This study proposes an integrated simulation framework that combines musculoskeletal multibody modeling, neural network-based muscle activation analysis, and hybrid motion tracking to investigate both normal and pathological gait and lower limb tremors. By bridging musculoskeletal biomechanics with neuromuscular control mechanisms, this approach enhances the accuracy of movement characterization and offers potential pathways for personalized therapeutic interventions. For gait analysis, foot-ground reaction forces were estimated through a contact model for soft materials. Joint torques and muscle forces were computed. In the context of Parkinson's disease, the study leveraged a neuromuscular modeling approach that integrates electromyographic (EMG) signals with a Matsuoka oscillator-driven dynamic model of the lower limb. The neural network-based framework enabled the generation of EMG-derived muscle activations to accelerometric signals, capturing the transition from neuromuscular excitation to mechanical tremor expression. By analyzing the biomechanical response of the lower limb, the multibody simulation framework clarified the underlying muscle activation patterns contributing to tremor manifestation. Through comparative simulations, the model refined its predictive capacity for individualized tremor or freezing of gait characterization, facilitating the development of targeted therapeutic strategies.

Developing subject-specific musculoskeletal models based on non-invasive in vivo measurements remains a significant challenge. Indeed, the muscular model utilized in this research allows for the personalization of simulations, adapting to individual subjects by incorporating specific anatomical and physiological data. This personalized approach enhances the accuracy and relevance of the simulations, facilitating more precise analysis of gait and tremor dynamics for each patient

The integration of musculoskeletal modeling with neuromuscular control principles provides a comprehensive toolset for analyzing movement disorders and optimizing rehabilitation approaches. This methodology not only improves the estimation of joint torques, musculotendon forces, tremor dynamics and freezing of gait but also enhances the understanding of pathological movement patterns. The results of this study demonstrate the potential for co-simulation techniques to bridge the gap between biomechanics and neuromuscular physiology, paving the way for real-time adaptive control applications in clinical settings. By providing deeper insights into the mechanical and neural foundations of movement, this approach holds promise for advancing personalized treatment strategies for gait abnormalities and Parkinsonian tremors.

Future studies on musculoskeletal multibody dynamics will aim to develop a more precise, intelligent, and subject-specific modeling framework. This emerging research direction will drive interdisciplinary collaborations between dynamics and biomechanics, contributing to more effective diagnostic and therapeutic tools for movement disorders.

Presenting Author: Antonio Zippo University of Modena and Reggio Emilia

Presenting Author Biography: Professor Antonio Zippo is an Associate Professor of Mechanism and Machine Theory, Applied Mechanics, and Mechanical Vibration at the "Enzo Ferrari" Department of Engineering, University of Modena and Reggio Emilia (UNIMORE). With a PhD in "Advanced Mechanics and Vehicle Techniques," he specializes in nonlinear dynamics, vibration analysis, and predictive maintenance. His research covers chaos theory, fluid-structure interactions, bioengineering, and nonlinear dynamics of Parkinson’s disease.
He has led and contributed to several high-profile projects, including NATO's CoMetA, THEORETIC, and REFIMAN, focusing on digital twins, predictive diagnostics, and sustainable technologies. His work has been supported by grants such as FAR2022 and CONSORZIO FUTURO IN RESEARCH for controlling pathological tremor dynamics.
Professor Zippo teaches courses in Multibody Dynamics, Mechanical Vibration, Prognostic and Predictive Maintenance, and more. He has published 83 articles, has an h-index of 13 with 410 citations, and was nationally qualified for Full Professor in 2023.
His recent research emphasizes nonlinear dynamic of gear, coupling of electric powertrains, Active Vibration Control of Brake Squeal, metamaterials and Parkinson’s Tremor modelling and control. He is part of the Vibration, NVH, and Powertrain Laboratory at UNIMORE.

Authors:

Antonio Zippo University of Modena and Reggio Emilia
Jianqiao Guo Beijing Institute of Technology
Yanbing Wang Beijing Institute of Technology
Francesco Pellicano Università di Modena e Reggio Emilia