Single-Scale EMA Latent Memory for Strictly Causal Dense Arm-Motion Recognition from Multi-Channel sEMG

Strictly causal dense arm-motion recognition from surface electromyography (sEMG) requires past context without future samples. We evaluated single-scale exponential moving average (EMA) latent memory as a lightweight causal temporal mechanism under a controlled, matched design. Causal CNN, causal CNN–Transformer (CT), and causal CNN with EMA memory (CEMA) were tested on a 30-subject, eight-channel, seven-class dataset in five subject-independent folds. At 300, 500, and 800 ms, five deterministic seeds were averaged within-fold before inference. CEMA accuracy exceeded CNN accuracy by 9.105, 9.313, and 9.657 percentage points, respectively (all Holm-adjusted paired-t p < 0.001). CEMA–CT differences were +0.209, −0.240, and −0.715 points, respectively; confidence intervals included zero, and directions varied across seeds. With parameters frozen, resetting memory at the current-window start reduced CEMA accuracy by 10.928–17.009 points. CEMA–CT B100 recall differences were −4.97, −12.84, and −16.54 points, respectively, whereas absolute-delay differences were 6.79, 7.71, and 6.94 ms, respectively, all below 8 ms. At 300 ms, CEMA used 6968 parameters and 0.751 M MACs, 48.0% fewer MACs than CT; CT required 2.31× CEMA’s full-buffer host-wall time. CEMA offers a favorable average accuracy–efficiency trade-off, although CT remains more responsive at transitions. Timing reflects full-buffer host inference only, not incremental, embedded, or end-to-end deployment.