Scaling Wrist sEMG for Hand Pose Estimation

Surface electromyography (sEMG) offers a promising alternative to vision for hand pose estimation, providing information that is complementary to visual observations. However, unlike vision, sEMG-based hand pose estimation has received limited attention. In this work, we present CycloFormer, a rotation-invariant Transformer that encodes the cyclic symmetry of the wrist-worn sEMG electrode ring into its architecture. It achieves state-of-the-art landmark-distance performance on emg2pose, and exhibits systematic scaling with model size and training data. Through comparison with the leading vision baseline, we show that an sEMG-based pose-estimation pipeline becomes more accurate when visual evidence is limited. We also derive a scaling law for sEMG hand-pose regression in the data-scarce regime. It identifies the loss-minimising model size at the current data scale and suggests that large-scale data collection is a key direction for advancing sEMG-based hand pose estimation.