Abstract
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.
Abstract (translated)
当前的外骨骼控制系统经常面临提供个性化治疗的挑战。标准化的行走步态可能会导致患者不适甚至受伤。因此，个性化的步态对于外骨骼机器人的有效性至关重要，因为它直接影响其对个体用户的适应性、舒适性和康复效果。为了在外骨骼辅助疗法及相关应用中实现个性化治疗，准确识别个人步态对于实施定制化步态控制至关重要。 在步态识别的关键挑战之一在于有效捕捉由关节协同作用引起的细微步态特征的个体差异，如步频和步长。为了解决这一问题，我们提出了一种新颖的方法，该方法使用多尺度全局密集图卷积网络（GCN）来识别潜在的关节协同模式，在空间领域中操作。此外，我们还提出了一个步态非线性周期动力学学习模块，以有效捕捉时间域内步态的周期特征。 为了支持我们的个体步态识别任务，我们构建了一个全面的步态数据集，确保其完整性和可靠性。实验结果显示，我们的方法在这个数据集中达到了94.34%的准确率，比当前最先进的（SOTA）技术高出3.77%，这一进步表明了我们的方法在外骨骼辅助治疗中提升个性化步态控制潜力的巨大前景。
URL
https://arxiv.org/abs/2505.18018
