Abstract
Multi-stream graph convolutional networks improve skeleton action recognition by combining representations, but each branch adds inference cost. We evaluate one fixed stream-removal operating point in a CTR-GCN pipeline using a paired non-inferiority protocol on NTU RGB+D 120. A temporal configuration reduced source-frame entries requested from the skeleton store by 53.52%, but lost 3.191 percentage points of accuracy and provided no latency saving after reopening the data mapping. A three-stream configuration then omitted bone motion while retaining 64 temporal positions. Historical RTX 4090 measurements at batch size one, using development checkpoints, showed 24.30–24.94% lower latency. After development confirmation, the fixed pair was trained on 54,468 examples and evaluated on 59,477 cross-setup test examples. Overall accuracy was 89.1050% for four streams and 89.0277% for three streams; paired setup-bootstrap lower bounds exceeded all three prespecified non-inferiority margins. Stream removal introduced 605 errors and corrected 559, while predicted labels changed for 1778 examples. Thus, a net loss of 46 correct decisions concealed broader redistribution. Recall decreased for 61 of 120 actions. These findings support assessing a branch by its contribution to the complete system, measured cost, and explicit quality tolerances. They do not imply preservation of every action. A separate CPU experiment with the final checkpoints measured 24.67% mean latency saving across three processes on one host. The contribution is a controlled empirical assessment, not a new architecture.