Same Gesture, Different Sensors: Cross-Dataset Representation Learning for Heterogeneous sEMG Gesture Recognition

Surface electromyography (sEMG) provides a promising sensing modality for wearable hand gesture recognition, yet existing datasets are typically studied in isolation because they differ in electrode configurations, channel counts, subjects, and acquisition conditions. We propose handling heterogeneous sEMG datasets as complementary sensor observations of shared gesture semantics. Our cross-dataset representation learning framework supports variable-channel inputs through channel-token representations and dataset-specific adapters. During heterogeneous source pre-training, masked channel-representation learning reduces dependence on individual sensor observations, while supervised cross-dataset contrastive learning explicitly aligns same-gesture representations across datasets. We then transfer the learned representation to a previously unseen target dataset through masked adaptation and supervised fine-tuning using only the target training split. Across five independently collected sEMG datasets spanning 2-16 channels, our source-only transfer approach improves average balanced accuracy from 77.52% under single-dataset training to 80.47%, with improvements on all five targets. These results show that shared gesture semantics provide a practical bridge for reusing knowledge across heterogeneous sEMG sensor configurations.