A Deep Forest and Histogram Feature Fusion Framework for sEMG-Based Hand Gesture Recognition with Enhanced Signal Representation

A novel hand gesture recognition framework based on surface electromyography (sEMG) is proposed for soldier operational scenarios under small-sample conditions. The framework integrates Empirical Mode Decomposition (EMD) for signal reconstruction, histogram-based features, and the Deep Forest (DF) classifier. Evaluations are conducted under two protocols: subject-wise evaluation and mixed-subject nested 8-fold cross-validation. Under subject-wise evaluation, the proposed EMD-HIST-DF method achieves 99.94% accuracy with 0.00027 ms per sample. Under mixed-subject nested 8-fold cross-validation, 98.41% accuracy is maintained with 0.00053 ms per sample. Ablation studies confirm the significant contribution of EMD-based signal enhancement in the mixed-subject setting (approximately 10.6 percentage points, p < 0.001). Parameter sensitivity analysis guides optimal parameter selection, and statistical tests confirm significant performance gains over baseline methods. Confusion matrices illustrate high per-class accuracy with minimal inter-class confusion. The framework shows potential as a promising solution for accurate, efficient, and sample-sparing gesture recognition in resource-constrained environments such as supernumerary robotic limb control.