A Deep Learning-Based Approach for Foot Placement Prediction
A Deep Learning-Based Approach for Foot Placement Prediction
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DOI：
10.1109/lra.2023.3290521
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发表时间：
2023-08
影响因子：
5.2
通讯作者：
Sung-Wook Lee;A. Asbeck
中科院分区：
文献类型：
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作者：
Sung-Wook Lee;A. Asbeck
足部放置预测对于外骨骼和假肢控制器、人机交互或身体穿戴系统可能是重要的，以防止滑倒或绊倒。以前的研究调查脚的位置预测已被限制在摆动阶段预测脚的位置，并没有充分考虑上下文信息，如前一步或前推离的立场阶段。在这项研究中，我们提出了一种基于深度学习的足部放置预测方法，顺序处理安装在骨盆和足部的三个IMU传感器的数据。对原始传感器数据进行预处理，以生成用于训练两个深度学习模型的多变量时间序列数据，其中第一个模型估计步态进展，第二个模型随后预测下一个足部放置。从运动捕捉系统获取地面实况步态相位数据和脚放置数据。10名健康受试者被邀请在跑步机上以不同的速度自然行走。在跨学科学习中，训练的模型在足部放置预测方面的平均距离误差为5.93 cm。在单受试者学习中，预测精度随着额外的训练数据而提高，并且通过使用目标受试者数据微调跨受试者验证模型，实现了2.60 cm的平均距离误差。即使在步态周期的25-81%，平均距离误差只有6.99厘米和3.22厘米的跨学科学习和单学科学习，分别。
Foot placement prediction can be important for exoskeleton and prosthesis controllers, human-robot interaction, or body-worn systems to prevent slips or trips. Previous studies investigating foot placement prediction have been limited to predicting foot placement during the swing phase, and do not fully consider contextual information such as the preceding step or the stance phase before push-off. In this study, we propose a deep learning-based foot placement prediction approach, sequentially processing data from three IMU sensors mounted on the pelvis and feet. The raw sensor data are pre-processed to generate multi-variable time-series data for training two deep learning models, where the first model estimates the gait progression and the second model subsequently predicts the next foot placement. The ground truth gait phase data and foot placement data are acquired from a motion capture system. Ten healthy subjects were invited to walk naturally at different speeds on a treadmill. In cross-subject learning, the trained models had a mean distance error of 5.93 cm for foot placement prediction. In single-subject learning, the prediction accuracy improved with additional training data, and a mean distance error of 2.60 cm was achieved by fine-tuning the cross-subject validated models with the target subject data. Even from 25–81% in the gait cycle, mean distance errors were only 6.99 cm and 3.22 cm for cross-subject learning and single-subject learning, respectively.