基于惯性测量单元的运动评估中标签模糊性的表示与检测
Representing and Detecting Label Ambiguity in IMU-Based Exercise Evaluation
- AI for Sensor Data Analytics Research Group, Ulm University of Applied Sciences（应用科学大学人工智能传感器数据分析研究组，乌尔姆大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究家庭理疗中运动评估问题，提出自动生成标签分布的方法，用Kullback-Leibler目标训练网络，与单热交叉熵基线对比，该方法在多数据集表现优且能可靠检测模糊性。
AI中文摘要：
家庭理疗无监督导致运动执行有误，促使基于惯性测量单元（IMU）的自动评估系统出现。此类系统给重复动作分类，但部分重复动作处于类别边界，即使训练有素的评分者也有分歧。用无需大量评分者库的方法自动生成每次重复的标签分布，用Kullback-Leibler目标训练网络，在四个IMU运动数据集上与单热交叉熵基线对比，该方法表现更优且能可靠检测模糊性。
英文摘要：
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.
