Human gait phase estimation has been studied in the field of robotics due to its importance in controlling wearable devices (e.g., robotic prostheses or exoskeletons) in a synchronized manner with the user. Researchers have attempted to estimate the user’s gait phase using a learning-based method, as data-driven approaches have recently emerged in the field. In this study, we propose a new labeling method (i.e., a piecewise linear label) to have the estimator learn the ground truth based on variable toe-off onset at different walking speeds. Using whole-body marker data, we computed the angular positions and velocities of thigh and torso segments and utilized them as input data for model training. Three models (i.e., general, slow, and normal-fast) were obtained based on long short-term memory (LSTM). These models are compared in order to identify the effect of the piecewise linear label at various walking speeds. As a result, when the proposed labeling method was used while training the general model, the estimation accuracy was significantly improved. This fact was also found when estimating the user’s gait phase during the mid-stance phase. Furthermore, the proposed method maintained good performance in detecting the heel-strike and toe-off. According to the findings of this study, the newly proposed labeling method could improve speed-adaptability in gait phase estimation, resulting in outstanding accuracy for both gait phase, heel-strike, and toe-off estimation.

中文翻译：

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CapMax：从多用户通信的角度进行动态网络表示学习的框架


人类步态相位估计因其在以与用户同步的方式控制可穿戴设备（例如，机器人假肢或外骨骼）方面的重要性而在机器人领域得到了研究。研究人员尝试使用基于学习的方法来估计用户的步态阶段，因为数据驱动的方法最近在该领域出现。在本研究中，我们提出了一种新的标记方法（即分段线性标签），使估计器基于不同步行速度下的可变脚趾离地起始点来学习基本事实。使用全身标记数据，我们计算了大腿和躯干部分的角度位置和速度，并将它们用作模型训练的输入数据。基于长短期记忆（LSTM）获得了三种模型（即一般、慢速和正常-快速）。比较这些模型是为了确定分段线性标签在不同步行速度下的效果。因此，当在训练通用模型时使用所提出的标记方法时，估计精度显着提高。在估计用户中间站立阶段的步态阶段时也发现了这一事实。此外，所提出的方法在检测脚跟着地和脚趾离地方面保持了良好的性能。根据这项研究的结果，新提出的标记方法可以提高步态阶段估计的速度适应性，从而使步态阶段、脚跟着地和脚趾离地估计都具有出色的准确性。