Abstract
Current electromyography (EMG) pattern recognition (PR) models have been shown to generalize poorly in unconstrained environments, setting back their adoption in applications such as hand gesture control. This problem is often due to limited training data, exacerbated by the use of supervised classification frameworks that are known to be suboptimal in such settings. In this work, we propose a shift to deep metric-based meta-learning in EMG PR to supervise the creation of meaningful and interpretable representations. We use a Siamese Deep Convolutional Neural Network (SDCNN) and contrastive triplet loss to learn an EMG feature embedding space that captures the distribution of the different classes. A nearest-centroid approach is subsequently employed for inference, relying on how closely a test sample aligns with the established data distributions. We derive a robust class proximity-based confidence estimator that leads to a better rejection of incorrect decisions, i.e. false positives, especially when operating beyond the training data domain. We show our approach's efficacy by testing the trained SDCNN's predictions and confidence estimations on unseen data, both in and out of the training domain. The evaluation metrics include the accuracy-rejection curve and the Kullback-Leibler divergence between the confidence distributions of accurate and inaccurate predictions. Outperforming comparable models on both metrics, our results demonstrate that the proposed meta-learning approach improves the classifier's precision in active decisions (after rejection), thus leading to better generalization and applicability.
Abstract (translated)
目前，用于手势控制的EMG（肌电图）模式识别（PR）模型在约束环境中的泛化能力较差，这使得它们在应用领域（如手势控制）中的采用受到了限制。这个问题通常是由于训练数据的有限性，以及使用已知在這種环境中表现不佳的监督分类框架而加剧的。在本文中，我们提出了一个从EMG PR转向深度基于指标的元学习，以指导有意义和可解释的表示的创建。我们使用Siamese Deep Convolutional Neural Network（SDCNN）和对比性三元组损失来学习捕捉不同类分布的EMG特征嵌入空间。接下来，我们采用最近邻算法进行推理，依赖于测试样本与已建立数据分布的接近程度。我们通过计算最近邻算法的置信度来得到一个鲁棒的分类器，可以更好地拒绝错误决策，即假阳性，尤其是在训练数据领域之外。我们通过在未见过的数据上测试训练后的SDCNN的预测和置信度来评估我们方法的有效性。评估指标包括准确率-拒绝曲线和准确与不准确预测之间的一致分布的Kullback-Leibler散度。在两个指标上超越相似模型的结果表明，所提出的元学习方法改进了分类器在积极决策（拒绝对话）中的准确率，从而提高了泛化能力和适用性。
URL
https://arxiv.org/abs/2404.15360
