Interpreting Deep Learning Features for Myoelectric Control: A Comparison With Handcrafted Features
Interpreting Deep Learning Features for Myoelectric Control: A Comparison With Handcrafted Features
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DOI：
10.3389/fbioe.2020.00158
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发表时间：
2020-03-03
影响因子：
5.7
通讯作者：
Scheme, Erik
中科院分区：
文献类型：
--
作者：
Cote-Allard, Ulysse;Campbell, Evan;Scheme, Erik
现有的肌电控制系统研究主要集中在通过设计手工特征来提取肌电信号的区分特征。然而，最近，深度学习技术已被应用于基于EMG的手势识别这一具有挑战性的任务。这些技术的采用慢慢地将重点从特征工程转移到特征学习。然而，深度学习的黑盒性质使得人们很难理解网络学习到的信息类型，以及它与手工特征的关系。此外，由于参与者之间EMG记录的高度可变性，使用标准训练方法，深度特征往往在受试者之间概括性较差。因此，这项工作引入了一种新的多域学习算法，称为ADANN（自适应域对抗神经网络），与标准训练相比，该算法显著提高了（p = 0.00004）受试者间分类精度，平均提高了19.40%。使用ADANN生成的特征，这项工作提供了第一个基于EMG的手势识别的拓扑数据分析，用于表征深度网络中编码的信息，使用手工制作的特征作为地标。该分析表明，手工制作的特征和学习的特征（在早期层中）都试图区分所有手势，但没有编码相同的信息来做到这一点。在后面的层中，学习到的特征倾向于采用one-vs.-一个给定类的所有策略。此外，通过使用卷积网络可视化技术，发现学习的特征实际上倾向于忽略收缩期间最活跃的通道，这与旨在捕获幅度信息的手工特征的流行形成鲜明对比。总的来说，这项工作通过提供一个明确的指导方针，在学习和手工制作的功能中编码的互补信息，为混合功能集铺平了道路。
Existing research on myoelectric control systems primarily focuses on extracting discriminative characteristics of the electromyographic (EMG) signal by designing handcrafted features. Recently, however, deep learning techniques have been applied to the challenging task of EMG-based gesture recognition. The adoption of these techniques slowly shifts the focus from feature engineering to feature learning. Nevertheless, the black-box nature of deep learning makes it hard to understand the type of information learned by the network and how it relates to handcrafted features. Additionally, due to the high variability in EMG recordings between participants, deep features tend to generalize poorly across subjects using standard training methods. Consequently, this work introduces a new multi-domain learning algorithm, named ADANN (Adaptive Domain Adversarial Neural Network), which significantly enhances (p = 0.00004) inter-subject classification accuracy by an average of 19.40% compared to standard training. Using ADANN-generated features, this work provides the first topological data analysis of EMG-based gesture recognition for the characterization of the information encoded within a deep network, using handcrafted features as landmarks. This analysis reveals that handcrafted features and the learned features (in the earlier layers) both try to discriminate between all gestures, but do not encode the same information to do so. In the later layers, the learned features are inclined to instead adopt a one-vs.-all strategy for a given class. Furthermore, by using convolutional network visualization techniques, it is revealed that learned features actually tend to ignore the most activated channel during contraction, which is in stark contrast with the prevalence of handcrafted features designed to capture amplitude information. Overall, this work paves the way for hybrid feature sets by providing a clear guideline of complementary information encoded within learned and handcrafted features.