用数据驱动方法实现中风后步态的自适应踝关节助力。
Towards Data-Driven Adaptive Exoskeleton Assistance for Post-stroke Gait
- 用多任务时序卷积网络，从三组惯性传感器数据估算踝关节扭矩。
- 在4名中风患者身上实现0.74±0.13的拟合优度，支持实时控制。
- 基于健康数据预训练，适合临床康复场景的可穿戴智能外骨骼。
近期研究显示，基于数据驱动方法的外骨骼能为健康年轻成年人动态适配多种任务的助力。然而，将其应用于存在神经肌肉步态障碍（如中风偏瘫）的人群仍具挑战性，这不仅源于人群异质性和步态变异性高，也因缺乏用于训练准确模型的中风步态数据集。尽管如此，数据驱动方法仍为控制提供了有前景的路径，有望使外骨骼在非结构化社区环境中安全有效运行。本工作首次实现了基于数据驱动扭矩估计的中风后步行时自适应跖屈与背屈助力。我们使用4名中风受试者在跑步机上采集的数据，训练了一个多任务时序卷积网络（TCN），模型利用三组惯性测量单元（IMU）数据，并在6名健康受试者的步行数据上进行预训练，得到$R^2 = 0.74 \± 0.13$。我们开发了可穿戴原型系统，实现在一名中风受试者上的实时感知、估计与执行，验证了该方法的可行性。
原文摘要 · Abstract (English)
Recent work has shown that exoskeletons controlled through data-driven methods can dynamically adapt assistance to various tasks for healthy young adults. However, applying these methods to populations with neuromotor gait deficits, such as post-stroke hemiparesis, is challenging. This is due not only to high population heterogeneity and gait variability but also to a lack of post-stroke gait datasets to train accurate models. Despite these challenges, data-driven methods offer a promising avenue for control, potentially allowing exoskeletons to function safely and effectively in unstructured community settings. This work presents a first step towards enabling adaptive plantarflexion and dorsiflexion assistance from data-driven torque estimation during post-stroke walking. We trained a multi-task Temporal Convolutional Network (TCN) using collected data from four post-stroke participants walking on a treadmill ($R^2$ of $0.74 \pm 0.13$). The model uses data from three inertial measurement units (IMU) and was pretrained on healthy walking data from 6 participants. We implemented a wearable prototype for our ankle torque estimation approach for exoskeleton control and demonstrated the viability of real-time sensing, estimation, and actuation with one post-stroke participant.
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