Chen Xiaowen, Martin Anne E
Mechanical Engineering Department, Pennsylvania State University, University Park, Pennsylvania, United States of America.
PLoS One. 2025 Feb 10;20(2):e0315186. doi: 10.1371/journal.pone.0315186. eCollection 2025.
Traditional gait event detection methods for heel strike and toe-off utilize thresholding with ground reaction force (GRF) or kinematic data, while recent methods tend to use neural networks. However, when subjects' walking behaviors are significantly altered by an assistive walking device, these detection methods tend to fail. Therefore, this paper introduces a new long short-term memory (LSTM)-based model for detecting gait events in subjects walking with a pair of custom ankle exoskeletons. This new model was developed by multiplying the weighted output of two LSTM models, one with GRF data as the input and one with heel marker height as input. The gait events were found using peak detection on the final model output. Compared to other machine learning algorithms, which use roughly 8:1 training-to-testing data ratio, this new model required only a 1:79 training-to-testing data ratio. The algorithm successfully detected over 98% of events within 16ms of manually identified events, which is greater than the 65% to 98% detection rate of previous LSTM algorithms. The high robustness and low training requirements of the model makes it an excellent tool for automated gait event detection for both exoskeleton-assisted and unassisted walking of healthy human subjects.
传统的足跟触地和足趾离地步态事件检测方法利用地面反作用力（GRF）或运动学数据进行阈值处理，而最近的方法倾向于使用神经网络。然而，当受试者的行走行为因辅助行走装置而显著改变时，这些检测方法往往会失效。因此，本文介绍了一种新的基于长短期记忆（LSTM）的模型，用于检测佩戴一对定制脚踝外骨骼行走的受试者的步态事件。这个新模型是通过将两个LSTM模型的加权输出相乘而开发的，一个以GRF数据作为输入，另一个以足跟标记高度作为输入。通过对最终模型输出进行峰值检测来发现步态事件。与其他使用大约8:1训练与测试数据比例的机器学习算法相比，这个新模型只需要1:79的训练与测试数据比例。该算法在手动识别事件的16毫秒内成功检测到超过98%的事件，高于先前LSTM算法65%至98%的检测率。该模型的高鲁棒性和低训练要求使其成为健康人类受试者在有外骨骼辅助和无辅助行走时自动步态事件检测的优秀工具。
