STAMP: TASK-STRUCTURED TRANSFORMERS FOR STREAMING SEMG-TO-POSE

Streaming hand-pose estimation from surface electromyography (sEMG) requires causal reconstruction from noisy, non-stationary signals. We propose STAMP, a task-structured Transformer that organizes electrode interactions, temporal evidence, pose evolution, and motion-dependent computation. SpatioTemporal Electrode Modeling (STEM) preserves the electrode axis and combines spatial softmax attention with temporally decayed causal linear attention, informed by sensing geometry. Affine Scan provides compact, parallelizable pose updates through gated propagation and correction, while history-conditioned Motion MoE adapts computation across timescales. On emg2pose, STAMP achieves the lowest cross-user mean angular and whole-hand landmark errors in every Regression and Tracking split among matched retrained implementations. Controlled replacements, including near-parameter-matched alternatives, support structured electrode modeling, historical expert selection, and complementary pose propagation and correction. At nearly matched capacity, STAMP has lower errors and steeper fitted scaling than vEMG2Pose over 32–158 training users, outperforming the 158-user baseline with only 95 users. Increasing capacity generally yields further gains within the measured grid. Dataset-specific retraining with interface-only adaptation also yields the lowest pooled errors on EgoEMG and NinaPro, supporting architectural portability across electrode layouts.