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.

翻译：机器人设备在高效可靠评估脑卒中后患者神经运动异常方面具有巨大潜力。然而，临床上对脑卒中引发的痉挛仍采用人工评估方式。从患者处采集的数据具有有限性和多变性，长期以来一直是利用机器人测量定量建模痉挛以及充分验证机器人评估技术的主要障碍。本文提出一种仿真框架，旨在支持肘关节痉挛模型的设计与验证，并缓解数据问题。该框架包含机器人辅助痉挛评估仿真环境、针对机器人与人体模型的两种运动控制器，以及牵张反射控制器。本框架支持基于合成数据开展仿真，无需受试者的实验数据。利用该框架，我们复现了机器人辅助痉挛评估中常用的恒速牵张实验，并对四类痉挛模型进行了评估。结果表明，与力依赖模型相比，同时包含肌纤维速度与长度反馈的痉挛反射模型能更准确地捕捉痉挛患者被动肘关节伸展过程中的关节阻力特性。当与合适的痉挛模型结合时，本仿真框架有望为未来痉挛评估研究生成大规模的虚拟患者数据集。