LSTM-based Network for Human Gait Stability Prediction in an Intelligent Robotic Rollator
LSTM-based Network for Human Gait Stability Prediction in an Intelligent Robotic Rollator
复制标题
基于 LSTM 的网络用于智能机器人助行车中的人类步态稳定性预测
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
P. Maragos
中科院分区：
文献类型：
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作者：
G. Chalvatzaki;Petros Koutras;Jack Hadfield;X. Papageorgiou;C. Tzafestas;P. Maragos
在这项工作中，我们提出了一种新的框架，用于使用长短期记忆（LSTM）网络对智能机器人助行车的老年用户进行在线人体步态稳定性预测，融合来自非可穿戴传感器的多模态RGB-D和激光测距仪（LRF）数据。基于深度学习（DL）的方法用于上身姿势估计。使用无迹卡尔曼滤波器（UKF）将检测到的姿势用于估计身体质心（CoM）。增强步态状态估计框架利用LRF数据来估计腿部的位置和相应的步态相位。这些估计是编码器-解码器序列到序列模型的输入，该模型预测步态稳定性状态为安全或跌倒风险行走。通过探索不同的网络架构、超参数设置以及将所提出的方法与其他基线进行比较，利用来自真实的患者的数据对其进行了验证。所提出的基于LSTM的人体步态稳定性预测器，以提供强大的预测的人体稳定状态，因此有可能被集成到一个通用的用户自适应控制架构作为跌倒风险警报。
In this work, we present a novel framework for on-line human gait stability prediction of the elderly users of an intelligent robotic rollator using Long Short Term Memory (LSTM) networks, fusing multimodal RGB-D and Laser Range Finder (LRF) data from non-wearable sensors. A Deep Learning (DL) based approach is used for the upper body pose estimation. The detected pose is used for estimating the body Center of Mass (CoM) using Unscented Kalman Filter (UKF). An Augmented Gait State Estimation framework exploits the LRF data to estimate the legs’ positions and the respective gait phase. These estimates are the inputs of an encoder-decoder sequence to sequence model which predicts the gait stability state as Safe or Fall Risk walking. It is validated with data from real patients, by exploring different network architectures, hyperparameter settings and by comparing the proposed method with other baselines. The presented LSTM-based human gait stability predictor is shown to provide robust predictions of the human stability state, and thus has the potential to be integrated into a general user-adaptive control architecture as a fall-risk alarm.
影响因子：
2.1
作者：
Hausdorff, Jeffrey M.
通讯作者：
Hausdorff, Jeffrey M.