Contrastive and Domain-Adaptive Evaluation of Control Laws Using Surface Electromyography During Exoskeleton-Assisted Walking

Accurate and real-time evaluation of energy expenditure is crucial for optimizing exoskeleton control laws. Conventional regression-based prediction approaches are strongly affected by inter-individual variability in surface electromyography (sEMG) signals, limiting their generalization across subjects. To address this limitation, we reformulate the evaluation task as a comparative classification problem, instead of predicting absolute metabolic values, the proposed method directly judges which of two control strategies induces lower energy expenditure. We design a Control Laws Evaluation Network (CLEN) based on a Siamese architecture, which captures pairwise sEMG representations to compare assistance strategies. To further mitigate subject-specific variability, we introduce a Dual Adversarial Adaptive Optimization Strategy (DAAOS) that aligns feature distributions across domains using maximum classifier discrepancy and domain confusion. Experimental results on both public and local datasets demonstrate that the proposed domain-adaptive framework significantly outperforms regression-based approaches, achieving accuracies of 77.6±3.1% on the public dataset and 73.3±4.7% on the local dataset across unseen subjects. The findings indicate that the proposed framework provides an effective and generalizable metric for optimizing exoskeleton control, with potential applications in mobility assistance.