研究：经验回放在 sEMG 手势识别跨用户持续学习中优于 EWC
Cross-User Adaptation for sEMG Gesture Recognition via Continual Learning
做肌电手势识别或持续学习的可以看看：在 NinaPro DB1 上对比了三种策略，经验回放遗忘率只有 EWC 的零头，还有训练窗口怎么切才不崩的细节。
一项发布在 arXiv 的研究在 NinaPro DB1 数据集上评估了三种持续学习策略，用于假肢界面中 sEMG 手势识别的 RNN 模型跨用户适配。结果显示经验回放在类增量漂移下达到 83.3% 最终准确率，遗忘仅 1.8-5.8%，而 EWC 和朴素微调的准确率下降达 16.2-28.5%。研究还发现模型必须使用打乱、独立窗口化的片段训练，按时间顺序流式输入会导致网络只预测单一主导手势。手工时域特征可将单任务准确率提升约 40%，但只有经验回放能在类增量漂移下保持这一增益。
Cross-User Adaptation for sEMG Gesture Recognition via Continual Learning
Prosthetic and assistive interfaces increasingly depend on decoding hand gestures from surface electromyography (sEMG). Because the signal is shaped by person-specific factors, the recurrent neural networks (RNNs) commonly used to model these temporal streams must adapt to new wearers and new gesture classes after deployment. Continual learning (CL) enables this adaptation without retraining from scratch, yet applying it to recurrent sEMG models is not straightforward. Standard recurrent training practices can destabilize learning, and which CL strategy holds up under realistic cross-user drift is unestablished. We evaluate three representative CL strategies under domain- and class-incremental drift on NinaPro DB1, and find experience replay to outperform elastic weight consolidation (EWC) and naive fine-tuning. It reaches 83.3% final accuracy for class-incremental drift while limiting forgetting to 1.8-5.8% accuracy drop, as against 16.2-28.5% for EWC and naive training. We further show these models must be trained on shuffled, independently-windowed segments. Streaming windows in temporal order collapses the network into predicting a single dominant gesture, while a shuffled, stateless regime trains stably. Finally, hand-crafted time-domain features raise per-task accuracy by up to about 40% over raw signals, but only replay preserves that gain under class-incremental drift.
