Core Concepts
A deep metric-based meta-learning framework is proposed to improve the robustness and interpretability of EMG-based hand gesture recognition models.
Abstract
The authors present a deep metric-based meta-learning approach to address the limitations of conventional classification frameworks in EMG-based hand gesture recognition (HGR). The key aspects of the proposed method are:
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Siamese Deep Convolutional Neural Network (SDCNN) Architecture:
- The SDCNN uses parallel branches of 2D convolutional layers with shared parameters to learn a semantically meaningful Euclidean feature embedding space.
- The network is trained using a contrastive triplet loss function, which enforces proximity between samples of the same class and maximizes the distance between samples of different classes.
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Nearest Centroid Classifier and Confidence Estimation:
- After training the SDCNN, a nearest centroid (NC) classifier is employed to perform multi-class discrimination based on the learned feature embeddings.
- The distance to each class centroid is used to derive a class membership score, providing a confidence estimate for the predictions.
The authors evaluate the proposed approach against several baseline models, including a standard DCNN, an SVM, and two other deep learning methods (CNNSC and ECNN), under three test scenarios:
- In-domain predictions
- Domain-divergent predictions (due to gesture transitions)
- Out-of-domain predictions (with unseen gesture classes)
The results demonstrate that the SDCNN-based approach outperforms the baseline models in terms of confidence-based decision rejection, as measured by metrics such as the accuracy-rejection curve (ARC) and Kullback-Leibler (KL) divergence between confidence distributions of accurate and inaccurate predictions. The authors also provide visualizations of the learned feature space, highlighting the interpretability of the SDCNN model.
The proposed framework shows promise for improving the robustness and usability of EMG-based HGR systems, particularly in unconstrained real-world environments. The transparent and distance-based confidence estimation can enable better rejection of incorrect decisions, leading to more reliable and practical EMG-based applications.
Stats
The mean absolute value (MAV) of each EMG channel is used as a measure of muscle contraction intensity.
The dynamic EMG sequences include gesture transitions, which can induce prediction errors due to domain divergence.
Quotes
"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."
"While acquiring larger EMG datasets to encompass a broader test domain may lead to better generalization, it is not practical due to the time and effort required from the end users to provide such data."
"Overconfidence is a fundamental problem with supervised classification frameworks, and have explored ways to better calibrate the networks."