用多尺度图网络识别用户步态差异，提升外骨骼个性化控制精度
ExoGait-MS: Learning Periodic Dynamics with Multi-Scale Graph Network for Exoskeleton Gait Recognition
- 结合时空双域建模，捕捉步态的周期性与关节协同特征
- 在自建数据集上达94.34%准确率，比现有最优方法高3.77%
- 适合外骨骼康复治疗、个性化步态控制等应用场景
当前外骨骼控制方法常因缺乏个性化而影响疗效，标准化步态易导致患者不适甚至受伤。因此，实现个性化步态识别对提升外骨骼适应性、舒适度及康复效果至关重要。本文提出一种新方法，通过多尺度全局密集图卷积网络（Multi-Scale Global Dense GCN）在空间域识别潜在的关节协同模式，并设计步态非线性周期动力学学习模块，在时间域有效捕捉步态的周期特性。为支持该任务，我们构建了一个完整且可靠的步态数据集。实验结果表明，该方法在该数据集上达到94.34%的识别准确率，优于当前最先进水平（SOTA）3.77%。这一成果凸显了其在个性化外骨骼步态控制中的应用潜力。
原文摘要 · Abstract (English)
Current exoskeleton control methods often face challenges in delivering personalized treatment. Standardized walking gaits can lead to patient discomfort or even injury. Therefore, personalized gait is essential for the effectiveness of exoskeleton robots, as it directly impacts their adaptability, comfort, and rehabilitation outcomes for individual users. To enable personalized treatment in exoskeleton-assisted therapy and related applications, accurate recognition of personal gait is crucial for implementing tailored gait control. The key challenge in gait recognition lies in effectively capturing individual differences in subtle gait features caused by joint synergy, such as step frequency and step length. To tackle this issue, we propose a novel approach, which uses Multi-Scale Global Dense Graph Convolutional Networks (GCN) in the spatial domain to identify latent joint synergy patterns. Moreover, we propose a Gait Non-linear Periodic Dynamics Learning module to effectively capture the periodic characteristics of gait in the temporal domain. To support our individual gait recognition task, we have constructed a comprehensive gait dataset that ensures both completeness and reliability. Our experimental results demonstrate that our method achieves an impressive accuracy of 94.34% on this dataset, surpassing the current state-of-the-art (SOTA) by 3.77%. This advancement underscores the potential of our approach to enhance personalized gait control in exoskeleton-assisted therapy.
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