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. Our project website, video, code, and dataset are available at: https://roamlab.github.io/reactemg-stroke/.

翻译：表面肌电信号（sEMG）是中风后按需辅助手部康复训练中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的受试者特异性校准，且对变异性鲁棒性较差。我们提出了一种从健康到卒中的自适应流程：该流程从一个基于大规模健康受试者sEMG数据预训练的意图检测器初始化，然后仅使用少量受试者特异性数据为每位卒中参与者进行微调。利用从三位慢性卒中患者中收集的新数据集，我们比较了多种自适应策略（仅头部微调、参数高效LoRA适配器以及全端到端微调），并在包含会话内漂移、姿势变化和臂带重新定位等实际分布偏移的保留测试集上进行评估。在各种条件下，与零样本迁移和相同数据预算下的仅卒中训练相比，基于健康预训练的自适应始终能改善卒中意图检测；最佳自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，迁移可复用的健康域EMG表征可以减少校准负担，同时提升实时卒中后意图检测的鲁棒性。我们的项目网站、视频、代码和数据集可在以下网址获取：https://roamlab.github.io/reactemg-stroke/。