From Continuous sEMG Signals to Discrete Muscle State Tokens: A Robust and Interpretable Representation Framework

2026-02-27

AI Summary

This study addresses the challenges of poor robustness and limited interpretability in surface electromyography (sEMG) signal decoding, which stem from high inter-subject variability and noise sensitivity. The authors propose a physiology-inspired discretization framework for sEMG: by aligning sliding windows to the minimal muscle contraction cycle, they extract ten-dimensional time–frequency features (e.g., RMS, MDF) and apply K-means clustering to generate muscle state tokens, establishing the first physiologically driven sEMG tokenization approach. Evaluated on the newly released multi-action, multi-muscle dataset ActionEMG-43, the method achieves high cross-subject consistency (Cohen’s Kappa = 0.82 ± 0.09) and yields Top-1 action recognition accuracies of 75.5% with a Vision Transformer and 67.9% with an SVM—substantially outperforming raw-signal baselines—while reducing input dimensionality by 96% and enabling interpretable analysis of movement quality.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Semi-Supervised Learning
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Surface electromyography (sEMG) signals exhibit substantial inter-subject variability and are highly susceptible to noise, posing challenges for robust and interpretable decoding. To address these limitations, we propose a discrete representation of sEMG signals based on a physiology-informed tokenization framework. The method employs a sliding window aligned with the minimal muscle contraction cycle to isolate individual muscle activation events. From each window, ten time-frequency features, including root mean square (RMS) and median frequency (MDF), are extracted, and K-means clustering is applied to group segments into representative muscle-state tokens. We also introduce a large-scale benchmark dataset, ActionEMG-43, comprising 43 diverse actions and sEMG recordings from 16 major muscle groups across the body. Based on this dataset, we conduct extensive evaluations to assess the inter-subject consistency, representation capacity, and interpretability of the proposed sEMG tokens. Our results show that the token representation exhibits high inter-subject consistency (Cohen's Kappa = 0.82+-0.09), indicating that the learned tokens capture consistent and subject-independent muscle activation patterns. In action recognition tasks, models using sEMG tokens achieve Top-1 accuracies of 75.5% with ViT and 67.9% with SVM, outperforming raw-signal baselines (72.8% and 64.4%, respectively), despite a 96% reduction in input dimensionality. In movement quality assessment, the tokens intuitively reveal patterns of muscle underactivation and compensatory activation, offering interpretable insights into neuromuscular control. Together, these findings highlight the effectiveness of tokenized sEMG representations as a compact, generalizable, and physiologically meaningful feature space for applications in rehabilitation, human-machine interaction, and motor function analysis.

Problem

• sEMG
• inter-subject variability
• noise susceptibility
• robust decoding
• interpretable representation

Innovation

• sEMG tokenization
• physiology-informed representation
• muscle state tokens
• inter-subject consistency
• ActionEMG-43