发表机构
University of California, Irvine（加州大学尔湾分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
REFIT通过轴变换族拟合与无标签准确率估计，无需标签即可识别并修复可穿戴传感器放置偏移，恢复大部分准确率并优于现有方法。
AI 中文摘要
我们提出REFIT，一种针对冻结活动识别模型的输入校准方法，这些模型在部署时惯性传感器的佩戴方式与训练时不同。当用户将手表移到另一只手腕或将带式传感器反向佩戴时，模型会在改变的轴上看到相同的运动。REFIT无需标签或重新训练即可消除此类偏移。它通过轴变换族（如反射和旋转）来描述这些偏移，并将每个变换族拟合到用户数据，使简单统计量与训练数据的统计量相匹配。最能消除失配的变换族即为所识别的偏移。REFIT通过在该冻结模型之前应用该变换族中的最佳成员并重新估计其归一化统计量来修复偏移。它使用无标签的准确率估计来测试修复后的模型，并在准确率较低时要求用户重新佩戴传感器。在真实左右传感器对以及真实和模拟的重新佩戴实验表明，REFIT在每个数据集上都优于无标签的测试时自适应方法，并恢复了因重新佩戴而损失的大部分准确率。它识别注入偏移的可靠性远高于基于置信度的选择器。在对所有轴的有符号排列进行校正后，该估计能区分成功与失败的校正。
英文摘要
We present REFIT, an input calibration for frozen activity-recognition models whose inertial sensors are worn differently at deployment than in training. When users move a watch to the other wrist or put a strap sensor back on turned, the model sees the same motion on changed axes. REFIT undoes such shifts without labels or retraining. It describes them by families of axis transforms, such as reflections and rotations, and fits each family to the user's data so that simple statistics match those of the training data. The family that removes most of the mismatch names the shift. REFIT fixes the shift by applying the best member of that family before the frozen model and re-estimating its normalization statistics. It tests the fixed model with a label-free accuracy estimate and asks the user to re-wear the sensor when it is low. Experiments on real left/right sensor pairs and on real and simulated re-attachment show that REFIT outperforms label-free test-time adaptation methods on every dataset and restores most of the accuracy lost to re-attachment. It names injected shifts far more reliably than a confidence-based selector. After a correction over all signed permutations of the axes, the estimate separates successful from failed corrections.
Comments25 pages, 5 figures, 14 tables. Under review
