A deep Kalman filter network for hand kinematics estimation using sEMG
A deep Kalman filter network for hand kinematics estimation using sEMG
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DOI：
10.1016/j.patrec.2021.01.001
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发表时间：
2021-01-21
影响因子：
5.1
通讯作者：
Zhang, Zhiqiang
中科院分区：
文献类型：
--
作者：
Bao, Tianzhe;Zhao, Yihui;Zhang, Zhiqiang
在人机界面（HMI）中，深度学习（DL）技术，如卷积神经网络（CNN），长短期记忆网络（LSTM）和混合CNN-LSTM框架已被用于使用表面肌电图（sEMG）进行手部运动学估计。然而，这些DL技术只捕捉sEMG和手部运动学之间的关系，但忽略了系统的先验知识。而卡尔曼滤波（KF）则可以通过引入卡尔曼增益，将联合收割机的内部过渡模型和观测模型有效地结合起来。为此，我们提出了一种名为深度卡尔曼滤波网络（DKFN）的新架构，其中我们利用CNN从sEMG中提取高级特征，并采用基于LSTM-KF的卡尔曼滤波过程（LSTM-KF）进行序列回归。特别是，LSTM-KF采用KF的计算图，但使用LSTM模块从数据中估计过渡/观测模型的参数和卡尔曼增益。通过这个过程，可以联合利用KF和LSTM的优势。实验结果表明，所提出的DKFN在手腕/手指运动学估计的序贯回归中的性能优于CNN和CNN-LSTM。(c)2021 Elsevier B. V.保留所有权利。
In human-machine interfaces (HMI), deep learning (DL) techniques such as convolutional neural networks (CNN), long-short term memory networks (LSTM) and the hybrid CNN-LSTM framework have been exploited for hand kinematics estimation using surface electromyography (sEMG). However, these DL techniques only capture the relationship between sEMG and hand kinematics, but ignores the prior knowledge of the system. By contrast, Kalman filter (KF) can apply Kalman gain to combine the internal transition model and the observation model effectively. To this end, we propose a novel architecture named deep Kalman filter network (DKFN), in which we utilize CNN to extract high-level features from sEMG and employ a LSTM-based Kalman filter process (LSTM-KF) to conduct sequential regression. In particular, LSTM-KF adopts the computational graph of KF but estimates parameters of the transition/observation model and the Kalman gain from data using LSTM modules. With this process, the advantages of KF and LSTM can be exploited jointly. Experimental results demonstrate that the proposed DKFN can outperform CNN and CNN-LSTM in the sequential regression for wrist/fingers kinematics estimation. (c) 2021 Elsevier B.V. All rights reserved.