Surface electromyography (sEMG) is a promising control signal for assist-as-needed hand rehabilitation after stroke, but detecting intent from paretic muscles often requires lengthy, subject-specific calibration and remains brittle to variability. We propose a healthy-to-stroke adaptation pipeline that initializes an intent detector from a model pretrained on large-scale able-bodied sEMG, then fine-tunes it for each stroke participant using only a small amount of subject-specific data. Using a newly collected dataset from three individuals with chronic stroke, we compare adaptation strategies (head-only tuning, parameter-efficient LoRA adapters, and full end-to-end fine-tuning) and evaluate on held-out test sets that include realistic distribution shifts such as within-session drift, posture changes, and armband repositioning. Across conditions, healthy-pretrained adaptation consistently improves stroke intent detection relative to both zero-shot transfer and stroke-only training under the same data budget; the best adaptation methods improve average transition accuracy from 0.42 to 0.61 and raw accuracy from 0.69 to 0.78. These results suggest that transferring a reusable healthy-domain EMG representation can reduce calibration burden while improving robustness for real-time post-stroke intent detection.

翻译：表面肌电图（sEMG）是中风后按需辅助手部康复的一种有前景的控制信号，但从麻痹肌肉中检测意图通常需要冗长且针对特定受试者的校准，并且对变异性仍然敏感。我们提出了一种从健康到中风的适应流程：首先利用在大规模健康人群sEMG数据上预训练的模型初始化意图检测器，然后仅使用少量特定受试者数据为每位中风参与者进行微调。基于新收集的三名慢性中风患者的数据集，我们比较了不同的适应策略（仅微调头部层、参数高效的LoRA适配器以及完整的端到端微调），并在包含现实分布偏移（如会话内漂移、姿势变化和臂带重新定位）的保留测试集上进行了评估。在各种条件下，相对于相同数据预算下的零样本迁移和仅使用中风数据的训练，基于健康数据预训练的适应方法持续提升了中风意图检测的性能；最佳适应方法将平均转换准确率从0.42提升至0.61，原始准确率从0.69提升至0.78。这些结果表明，迁移可复用的健康领域肌电图表征能够减少校准负担，同时提升实时中风后意图检测的鲁棒性。