KinEMbed: Decoding Kinematics from Electromyography via Cross-Modal Contrastive Learning
For prosthetic control and motor rehabilitation, this work provides a first step toward contrastive regression of continuous kinematics from EMG, though it is incremental over existing methods.
KinEMbed uses cross-modal contrastive learning to regress hand kinematics from EMG, outperforming PCA, PLS, autoencoder, and CEBRA baselines on NinaPro DB8, with largest gains on thumb articulation.
Decoding hand kinematics from surface electromyography (EMG) is a core challenge in wearable biosignal processing with clinical relevance for prosthetic control and motor rehabilitation. Most representation learning approaches for EMG focus on discrete gesture classification, and few focus on continuous regression. We present KinEMbed, a cross-modal contrastive learning framework for hand kinematics regression that jointly trains dual encoders -- one for windowed EMG features and one for kinematic (joint angle) targets. The resulting embeddings inherit the geometric structure of the kinematic space without requiring kinematic signals at inference time. Evaluating on the NinaPro DB8 dataset that includes both able-bodied users and subjects with limb difference (N=11), KinEMbed outperforms PCA, PLS, autoencoder and contrastive (CEBRA) baselines on held-out sessions, with largest gains on the most challenging thumb degrees of articulation. We position this work as a first step toward contrastive representation learning for regression of hand kinematics from structured wearable biosignals.