Self-Supervised Learning via VICReg Enables Training of EMG Pattern Recognition Using Continuous Data with Unclear Labels

TL;DR

This paper demonstrates that self-supervised learning with VICReg and LSTM models improves electromyography pattern recognition on continuous data with transitions, outperforming traditional methods and highlighting the importance of dynamic data for training.

Contribution

It introduces a self-supervised pre-training approach using VICReg for LSTM-based sEMG pattern recognition on continuous data with transitions, showing significant performance improvements.

Findings

Temporal models outperform non-temporal models on continuous data.

VICReg pre-training significantly enhances model performance.

Model performance depends on data type and dynamics.

Abstract

In this study, we investigate the application of self-supervised learning via pre-trained Long Short-Term Memory (LSTM) networks for training surface electromyography pattern recognition models (sEMG-PR) using dynamic data with transitions. While labeling such data poses challenges due to the absence of ground-truth labels during transitions between classes, self-supervised pre-training offers a way to circumvent this issue. We compare the performance of LSTMs trained with either fully-supervised or self-supervised loss to a conventional non-temporal model (LDA) on two data types: segmented ramp data (lacking transition information) and continuous dynamic data inclusive of class transitions. Statistical analysis reveals that the temporal models outperform non-temporal models when trained with continuous dynamic data. Additionally, the proposed VICReg pre-trained temporal model with continuous dynamic data significantly outperformed all other models. Interestingly, when using only ramp data, the LSTM performed worse than the LDA, suggesting potential overfitting due to the absence of sufficient dynamics. This highlights the interplay between data type and model choice. Overall, this work highlights the importance of representative dynamics in training data and the potential for leveraging self-supervised approaches to enhance sEMG-PR models.