New Synthetic Goldmine: Hand Joint Angle-Driven EMG Data Generation Framework for Micro-Gesture Recognition

TL;DR

This work tackles the scarcity and variability of surface EMG data for hand-gesture recognition by introducing SeqEMG-GAN, a conditional GAN that generates EMG sequences from hand joint angle trajectories. It combines a novel Ang2Gist context encoder with a multi-perspective discriminator and KL regularization to ensure temporal coherence, semantic alignment, and physiologic plausibility. Experimental results on emg2pose demonstrate that augmenting real data with SeqEMG-GAN synthetic samples improves classification performance and cross-user generalization, while synthetic data alone offers limited gains. The approach sets a new standard for physiology-aware EMG data augmentation and has practical implications for neural control interfaces and AR/VR gesture systems.

Abstract

Electromyography (EMG)-based gesture recognition has emerged as a promising approach for human-computer interaction. However, its performance is often limited by the scarcity of labeled EMG data, significant cross-user variability, and poor generalization to unseen gestures. To address these challenges, we propose SeqEMG-GAN, a conditional, sequence-driven generative framework that synthesizes high-fidelity EMG signals from hand joint angle sequences. Our method introduces a context-aware architecture composed of an angle encoder, a dual-layer context encoder featuring the novel Ang2Gist unit, a deep convolutional EMG generator, and a discriminator, all jointly optimized via adversarial learning. By conditioning on joint kinematic trajectories, SeqEMG-GAN is capable of generating semantically consistent EMG sequences, even for previously unseen gestures, thereby enhancing data diversity and physiological plausibility. Experimental results show that classifiers trained solely on synthetic data experience only a slight accuracy drop (from 57.77\% to 55.71\%). In contrast, training with a combination of real and synthetic data significantly improves accuracy to 60.53\%, outperforming real-only training by 2.76\%. These findings demonstrate the effectiveness of our framework,also achieves the state-of-art performance in augmenting EMG datasets and enhancing gesture recognition performance for applications such as neural robotic hand control, AI/AR glasses, and gesture-based virtual gaming systems.

Figures (2)

• Figure 1: Overview of the SeqEMG-GAN framework. Joint angle sequences are encoded into a global latent variable and contextual hidden states, which guide the generator in synthesizing EMG signals. A discriminator jointly evaluates temporal and semantic alignment.
• Figure 2: Synthesized EMG Gesture Data and Its Visualization Results by SeqEMG-GAN