A Causal Learning-Based sEMG Disentanglement Framework for Multi-Posture Domain Generalization.

Abstract

Surface electromyography (sEMG) -based human-computer interaction (HCI) systems achieve high accuracy in controlled environments, but their robustness under daily life remains challenging. In real-world scenarios, variations in user posture introduce personalized biases that can significantly degrade model performance. A viable solution is to train a highly generalized network using existing data from various postures, enabling the model to become less sensitive to posture variations. In this work, we treat the original sEMG signals as a coupling of pattern and posture components, where each component can be considered as a causal signal specific to corresponding labels. We use the causal encoders to understand the generative relationships between data and labels, facilitating the disentanglement of components into different latent spaces and promoting clustering within each space. This enables the model to extract posture-invariant pattern components and train a robust pattern recognition model with strong generalization capabilities. We developed a high-density sEMG (HD-sEMG) dataset with 16 subjects performing in four common HCI postures, addressing the lack of posture variation samples in existing sEMG datasets. Our model achieved an average accuracy of 90.3% across four generalization tasks, outperforming other domain generalization models and demonstrating its superiority.