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arXiv · 2412.04700

SpasticMyoElbow: Physical Human-Robot Interaction Simulation Framework for Modelling Elbow Spasticity

Hao Yu·Zebin Huang·Yutong Li·Xinliang Guo·Vincent Crocher·Ignacio Carlucho·Mustafa Suphi Erden
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Abstract

Robotic devices hold great potential for efficient and reliable assessment of neuromotor abnormalities in post-stroke patients. However, spasticity caused by stroke is still assessed manually in clinical settings. The limited and variable nature of data collected from patients has long posed a major barrier to quantitatively modelling spasticity with robotic measurements and fully validating robotic assessment techniques. This paper presents a simulation framework developed to support the design and validation of elbow spasticity models and mitigate data problems. The framework consists of a simulation environment of robot-assisted spasticity assessment, two motion controllers for the robot and human models, and a stretch reflex controller. Our framework allows simulation based on synthetic data without experimental data from human subjects. Using this framework, we replicated the constant-velocity stretch experiment typically used in robot-assisted spasticity assessment and evaluated four types of spasticity models. Our results show that a spasticity reflex model incorporating feedback on both muscle fibre velocity and length more accurately captures joint resistance characteristics during passive elbow stretching in spastic patients than a force-dependent model. When integrated with an appropriate spasticity model, this simulation framework has the potential to generate extensive datasets of virtual patients for future research on spasticity assessment.

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Hao Yu, Zebin Huang, Yutong Li, Xinliang Guo, Vincent Crocher, Ignacio Carlucho, Mustafa Suphi Erden. 2024-12-06. SpasticMyoElbow: Physical Human-Robot Interaction Simulation Framework for Modelling Elbow Spasticity. https://arxiv.org/abs/2412.04700

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