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
本研究比较四种利用小校准预算个性化跨用户肌电编码器的方法，发现全微调最准确，但无梯度原型规则可在无优化器下恢复大部分收益，便于穿戴设备佩戴时个性化。
AI 中文摘要
肌电接口在使用前需要用户进行校准。早期工作将校准视为一种数量，但未提出设备在收集到校准重复数据后应如何处理这些数据的问题。本文将跨用户编码器的个性化视为一项具有成本的设计决策。针对每个留出受试者，从单个跨用户编码器出发，测试了四种使用完全相同标记重复数据的替代方法。在每种数据库允许的最大预算下，即DB1上的五次重复和DB2及DB5上的四次重复，测试了原型适应、线性探针、缩放微调和全微调。通过比较77名受试者上花费小校准预算的四种方式，全微调在每个预算下都最为准确，其一致性足以在受试者子集中无一例外。然而，影响工程决策的结果是，无梯度的原型规则在无优化器和无每用户权重副本的情况下，恢复了其收益的52%至78%，这使得个性化可以在穿戴设备佩戴时进行。关于良好表示仅需新分类器的普遍直觉在此处不正确。每种方法相对于由诊所拟合的每用户分类器的表现将取决于具体数据库。
英文摘要
A myoelectric interface needs calibration from the user before it will function. Earlier work has treated calibration as a quantity, but has not asked the question of what a device should do with the calibration repetitions once they have been collected. This paper views personalizing the cross-user encoder as a design decision with a cost. Four alternative approaches to using exactly the same labeled repetitions were tested from a single cross-user encoder per held-out subject. Prototypical adaptation, linear probes, scaled fine-tuning and full fine-tuning were tested at every budget up to the maximum each database allows, five repetitions on DB1 and four on DB2 and DB5. Comparing four ways to spend a small calibration budget across 77 subjects, full fine-tuning is the most accurate at every budget, consistently enough that there is no exception among subsets of subjects. The result which impacts how one might make an engineering decision however is that a gradient free prototypical rule recovers 52 to 78 per cent of its benefit with no optimiser and no per-user copy of the weights, which makes personalisation something a worn device can do at donning time. The widespread intuition that a good representation only needs a fresh classifier is incorrect here. How well each method may perform relative to a per-user classifier that would be fitted by a clinic will depend on the specific database.
Comments28 pages, 7 figures, 5 tables
