用仿瘫痪服模拟中风步态，加速康复机器人设计。
Analyzing Gait Adaptation with Hemiplegia Simulation Suits and Digital Twins
- 健康人穿仿真服在受控环境测试步态变化。
- 仿真服使动作更突兀，肌肉激活模式显著改变。
- 数字孪生模型可精准识别使用行为与步态异常。
为推动助行与康复机器人的早期研发，需在设计初期进行实验，但直接在用户身上测试原型存在安全风险。为此，本文探索让健康参与者穿戴特定功能仿真服，在可控环境中模拟不同损伤状态下的步态变化，支持快速原型开发。研究通过Vicon运动捕捉系统及Delsys Trigno EMG与IMU传感器，在四种行走条件下（是否使用助行器、是否穿戴仿真服）采集生物力学数据。将步态数据整合至数字孪生模型中，利用机器学习分析识别仿真服与助行器的使用状态、转弯行为，并评估仿真服对步态的长期影响。结果表明，仿真服显著改变运动模式与肌肉激活模式，促使使用者采用更突兀的代偿动作。研究还识别出最有效的特征与传感器模态，可用于准确建模人体-助行器交互与步态动态。
原文摘要 · Abstract (English)
To advance the development of assistive and rehabilitation robots, it is essential to conduct experiments early in the design cycle. However, testing early prototypes directly with users can pose safety risks. To address this, we explore the use of condition-specific simulation suits worn by healthy participants in controlled environments as a means to study gait changes associated with various impairments and support rapid prototyping. This paper presents a study analyzing the impact of a hemiplegia simulation suit on gait. We collected biomechanical data using a Vicon motion capture system and Delsys Trigno EMG and IMU sensors under four walking conditions: with and without a rollator, and with and without the simulation suit. The gait data was integrated into a digital twin model, enabling machine learning analyses to detect the use of the simulation suit and rollator, identify turning behavior, and evaluate how the suit affects gait over time. Our findings show that the simulation suit significantly alters movement and muscle activation patterns, prompting users to compensate with more abrupt motions. We also identify key features and sensor modalities that are most informative for accurately capturing gait dynamics and modeling human-rollator interaction within the digital twin framework.
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