发表机构
University of Saskatchewan（萨斯喀彻温大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对表面肌电手势解码中跨会话的性能下降问题，提出电极配置无关编码器，在NinaPro DB6数据集上验证其性能优于对比方法，且特征统计对齐的无标签适配可提升所有受试者表现。
AI 中文摘要
在表面肌电手势解码中，当用户取下电极后再次佩戴时，某一记录会话中获得的识别准确率无法保持。电极可能轻微移动、皮肤可能更干燥或湿润、肘部位置可能不同，这些因素都会导致日常变异性，因此是在日常实践中实现基于模式识别的成功肌电控制系统的主要障碍。然而，期望用户在每次脱戴电极时都花20分钟重新校准手部显然不现实。本文使用特定记录会话收集的数据训练了一个专为跨用户、跨电极配置迁移设计的电极配置无关编码器，随后将其直接应用于后续不同记录会话收集的数据，无需任何调整，实验对象为NinaPro DB6数据集中的10名完整受试者。将该方法的性能与每用户LDA分类流水线以及两种仅依赖同一记录会话源数据的已发表方法进行比较。该编码器未经调整跨会话应用时，宏F1值为0.688，而每用户流水线为0.540；在已发表基线使用的逐窗口指标上，其结果处于两个仅源数据结果的区间之上，该区间跨度为2个百分点，可用于定位编码器的性能而非仅排名。在五种无标签测试时间适配方法中，仅特征统计对齐对所有受试者均有提升；批归一化重估计（领域适应文献中的标准方法）会使该架构的性能完全崩溃。将编码器的特征统计与新会话对齐后，其性能恢复效果约相当于一次带标签的校准重复。
英文摘要
Recognition accuracy obtained during a recording session does not persist when a user puts on the electrodes again after the electrodes had previously been removed. The electrodes may have moved slightly, the skin may be drier or wetter, or the elbow may be positioned differently; these factors all contribute to day-to-day variability and therefore represent a major obstacle to implementing successful pattern-recognition based myoelectric control systems in daily practice. However, simply recalibrating a user's hand for 20 min at every doff/don event is a clearly unrealistic expectation. A montage-agnostic encoder built for cross-user, cross-montage transfer is trained here using data collected during a particular recording session, and then applied to data collected later in a different recording session without adjusting anything, on the ten intact subjects of NinaPro DB6. The performance of this approach is compared to that of a per-user LDA classification pipeline, and to that of two published approaches that only rely on source data collected from the same recording session. Carried unchanged across recording sessions, the encoder retains 0.688 macro-F1 against 0.540 for the per-user pipeline, and, on the per-window metric the published baselines use, sits above both published source-only results, a band of two points that locates the encoder rather than ranking it. Of five label-free test-time adaptations, only feature-statistic alignment improves every subject; batch-normalisation re-estimation, a standard method in the domain-adaptation literature, collapses this architecture entirely. Aligning the encoder's feature statistics to the new session recovers about what a single labelled calibration repetition would.
Comments23 pages, 6 figures, 6 tables
