用标签分布代替单一标签，让动作评估系统识别模糊动作并提升准确性。
Representing and Detecting Label Ambiguity in IMU-Based Exercise Evaluation
- 用分布标签替代单标签，通过KL散度训练网络捕捉动作模糊性。
- 在4个数据集上分类效果不差于传统方法，且更准确检测模糊动作。
- 适合需要精细动作评估的居家康复场景，无需大量人工标注。
居家物理治疗缺乏监督，易导致动作执行错误，促使人们开发基于惯性测量单元（IMUs）的自动运动评估系统。这类系统将每个重复动作归类，但相当一部分动作位于类别边界附近，即使受过训练的评估者也存在分歧。使用单标签（one-hot）训练的分类器会将这些边界动作强行归入某一类，从而丢失模糊性信息。本文提出一种方法，可在无需大规模评分员的情况下自动生成每个动作的标签分布。通过训练网络以最小化Kullback-Leibler散度来还原完整分布，即“模糊性方法”，并与one-hot交叉熵基线在四个IMU运动数据集上进行对比。从网络输出可进一步判断动作是否模糊及其相关类别。结果表明，该方法在所有四个数据集上的分类性能匹配或超越基线，并更可靠地检测模糊性和相关类别。因此，在训练目标中表示标签分布，可在不损害分类性能的前提下，有效传递模糊性信息。
原文摘要 · Abstract (English)
Home-based physiotherapy is performed without supervision, which leads to incorrect execution and motivates systems that assess movement automatically from inertial measurement units (IMUs). Such systems assign each repetition to a category, yet a relevant share of repetitions falls near a class boundary, where even trained raters disagree. Classifiers trained with one-hot labels collapse these borderline repetitions onto a single class and discard this ambiguity. We address this with a method that automatically generates a label distribution per repetition without a large rater pool. We train a network to reproduce the full distribution with a Kullback-Leibler objective, the ambiguity approach, and compare it against a one-hot cross-entropy baseline on four IMU exercise datasets. From the network output we further determine whether a repetition is ambiguous and which classes are relevant to it. The ambiguity approach matched or exceeded the baseline classification on all four datasets, and detected ambiguity and the relevant classes more reliably. Representing the label distribution in the training target therefore adds information about ambiguity at no cost to classification.
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