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

TL;DR

This work introduces SpasticMyoElbow, a forward, physics-based simulation framework for robot-assisted elbow spasticity assessment that enables synthetic data-driven validation without human experiments. Built on MyoSuite, it couples a robotic exoskeleton controller with a stretch reflex module and tests four spasticity representations, including a length-velocity hybrid model that most accurately reproduces passive elbow resistance. The framework supports replicating constant-velocity stretch protocols and provides a scalable, open platform for systematic comparison of spasticity models, offering potential to generate large virtual patient datasets for future research. While not clinically validated due to data limitations, the approach yields insights into spasticity mechanisms and highlights the computational efficiency and flexibility of forward simulation for HRI-based spasticity assessment and model validation.

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.

Figures (5)

• Figure 1: Framework diagram of SpasticMyoElbow. The physical interaction between the robot and the musculoskeletal model during a ramp-and-hold extension can be simulated through the parallel control of the robot and human controllers within a visual simulation environment. The stretch reflex controller allows the elbow model to exhibit spasticity symptoms, simulating a virtual patient in the interaction. This setup provides a validation dataset for evaluating spasticity models.
• Figure 2: Simulation data of the robot-assisted constant-velocity elbow stretch experiments: a) The angle profiles of the ramp-and-hold extension movements; b) The driving torques collected in the simulated elbow stretch experiment.
• Figure 3: (a-c) Reflex torque curves plotted against joint angle for the length-dependent spasticity model, with reflex gains ranging from 1 to 3. (e-g) Reflex torque curves plotted against joint angle for the velocity-dependent spasticity model, with reflex gains ranging from 1 to 3. (i-k) Reflex torque curves plotted against joint angle for the force-dependent spasticity model, with reflex gains ranging from 1 to 3. (d, h, l) Evolution of reflex torques over time for the length-, velocity-, and force-dependent spasticity models, with a reflex gain of 3.
• Figure 4: Muscle excitations of the long head of the biceps brachii modulated by the length-, velocity-, and force-dependent spasticity models under a 90$^\circ/s$ elbow stretch.
• Figure 5: Reflex torque curves plotted against joint angle for the hybrid spasticity model with different model parameters ($G$: gains, $\lambda$: threshold factor) at 90$^\circ/s$. The reference torque curves are extracted from McPherson et al. mcphersonBiomechanicalParametersElbow2019.