EMGBlend: Heterogeneity-Aware Self-Supervised Pretraining for Gesture and Force Decoding

2026-09-21

AI Summary

为解决EMG数据集异质性问题，提出EMGBlend框架，通过几何感知注意力和频谱目标限制等方法进行自监督预训练，提升手势识别与力解码效果。

Abstract

Public surface electromyography (EMG) datasets vary widely in electrode layout, channel count, frequency support, and size. Simply mixing them for pretraining can misalign channel semantics, introduce spectral targets that some devices cannot observe, and let large or high-channel-count datasets dominate learning. We introduce EMGBlend, a self-supervised framework designed around these differences. It combines shared channel patches with geometry-aware attention, restricts spectral targets to each recording's supported frequency band, and balances exposure across data sources. We pretrain a 109M-parameter model on 11 public EMG sources and evaluate it on gesture recognition, continuous-force regression, and contact classification. EMGBlend consistently outperforms matched random initialization and waveform reconstruction controls. Fixed-budget source controls show that multi-source pretraining improves gesture recognition and remains competitive for force decoding. Ablations confirm that geometry, band-aware targets, and source balancing each contribute to transfer, although cross-person NinaPro force estimation remains difficult. Overall, EMGBlend shows how heterogeneous EMG datasets can be combined through explicit mechanism design rather than simple concatenation. Code is available at https://github.com/tamanano/EMGBlend

Problem

• surface electromyography
• heterogeneity
• pretraining

Innovation

• self-supervised framework
• geometry-aware attention
• spectral targets
• source balancing
• heterogeneous EMG datasets