Early Recognition and Temporal Stability in Three-Channel Low-Density EMG: Impact of Segmentation Strategies for Real-Time Myoelectric Interfaces

Abstract

Wearable electromyography (EMG) interfaces enable natural human-robot interaction, and low-density configurations are preferred for practical deployment due to their simplicity and cost-effectiveness. However, limited spatial information challenges reliable and timely pattern recognition. This study compares three segmentation strategies for three-channel EMG gesture recognition - fixed sliding window, event-gated adaptive thresholding, and Teager-Kaiser energy operator (TKEO) - evaluated on six gestures from eight subjects under leave-one-subject-out cross-validation with BiLSTM+Attention classification. Mean classification accuracies ranged from 59.17% (fixed window) to 64.39% (adaptive), with no statistically significant differences among methods (p > 0.18). Temporal evaluation showed mean onset-to-decision latencies of 113.9 ms (TKEO), 137.7 ms (fixed window), and 149.7 ms (adaptive) - all within the 300 ms real-time feasibility threshold. Cross-subject variability (26-88%) substantially exceeded inter-method differences, suggesting that subject-specific factors are a more critical performance determinant than segmentation strategy. These results are intended as a direction-confirming study for temporal processing design in low-channel wearable EMG systems; statistical generalization to larger populations is reserved for future work.