Parkinson's disease is a neurodegenerative disorder characterized by motor symptoms such as tremors, rigidity, and bradykinesia. Tremors significantly impact the quality of life for individuals affected by the disease, making it crucial to explore innovative approaches for effectively improving the Activities of Daily Living (ADL). This study focuses on the dynamic modeling of the human upper limb to understand and control Parkinsonian tremors. On the first the study focuses on the development of a dynamic model representing the upper limb, considering its complex biomechanics and neuromuscular dynamics. Starting from experimentally measured signals, the model emulates the intricate interplay between the electromyographic (EMG) signal and musculoskeletal system dynam- ics, by incorporating neural network algorithms and providing a realistic simulation of Parkinsonian tremor. This dynamic model helps as a valuable tool for comprehending the underlying mechanisms of tremor in Parkinson's disease. The second component of the study employs multibody simulation to analyze the mechanical aspects of tremors in the upper limb. Through several simulations compared with experimental measurements, the investigation high- lights the impact of EMG signals on joint movements, muscle contractions, and overall limb dynamics. This com- prehensive approach allows for a deeper understanding of the biomechanical factors contributing to Parkinsonian tremor. Furthermore, the integration of neural network control mechanisms into the dynamic model opens avenues for exploring novel therapeutic interventions. By leveraging real-time feedback and adaptive control strategies, the neural network aims to modulate the simulated tremor, providing insights into potential strategies for tremor sup- pression in actual clinical settings. In conclusion, the combination of neural network modeling and multibody co-simulation not only enhances our understanding of the complex interactions involved but also presents opportunities for the development of targeted interventions aimed at mitigating the impact of tremors in individuals with Parkinson's disease.
HUMAN UPPER LIMB MODELLING OF PARKINSONIAN TREMOR THROUGH NEURAL NETWORK AND MULTIBODY SIMULATION / Zippo, A.; Pellicano, F.. - (2024). ( ICSV30 Amsterdam 8-11 luglio 2024).
HUMAN UPPER LIMB MODELLING OF PARKINSONIAN TREMOR THROUGH NEURAL NETWORK AND MULTIBODY SIMULATION
Zippo A.;Pellicano F.
2024
Abstract
Parkinson's disease is a neurodegenerative disorder characterized by motor symptoms such as tremors, rigidity, and bradykinesia. Tremors significantly impact the quality of life for individuals affected by the disease, making it crucial to explore innovative approaches for effectively improving the Activities of Daily Living (ADL). This study focuses on the dynamic modeling of the human upper limb to understand and control Parkinsonian tremors. On the first the study focuses on the development of a dynamic model representing the upper limb, considering its complex biomechanics and neuromuscular dynamics. Starting from experimentally measured signals, the model emulates the intricate interplay between the electromyographic (EMG) signal and musculoskeletal system dynam- ics, by incorporating neural network algorithms and providing a realistic simulation of Parkinsonian tremor. This dynamic model helps as a valuable tool for comprehending the underlying mechanisms of tremor in Parkinson's disease. The second component of the study employs multibody simulation to analyze the mechanical aspects of tremors in the upper limb. Through several simulations compared with experimental measurements, the investigation high- lights the impact of EMG signals on joint movements, muscle contractions, and overall limb dynamics. This com- prehensive approach allows for a deeper understanding of the biomechanical factors contributing to Parkinsonian tremor. Furthermore, the integration of neural network control mechanisms into the dynamic model opens avenues for exploring novel therapeutic interventions. By leveraging real-time feedback and adaptive control strategies, the neural network aims to modulate the simulated tremor, providing insights into potential strategies for tremor sup- pression in actual clinical settings. In conclusion, the combination of neural network modeling and multibody co-simulation not only enhances our understanding of the complex interactions involved but also presents opportunities for the development of targeted interventions aimed at mitigating the impact of tremors in individuals with Parkinson's disease.Pubblicazioni consigliate

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