面向足球应用的活动识别中通过投影后处理改进状态估计
Improving state estimation through projection post-processing for activity recognition with application to football
AI总结：
该研究针对传感器数据活动识别的标注与评估需求，提出修正短时长活动的投影后处理方法和适配状态转换时刻不确定性的LTS度量，经模拟与足球真实数据验证有效，并讨论了参数选择。
摘要：
过去十年间，基于传感器数据的人体活动识别受到了越来越多的关注。通常情况下，传感器数据都是未标注的，因此需要快速标注方法。为评估标注质量，必须选择合适的性能指标。我们的主要贡献是提出了一种用于活动识别的新型后处理方法，它通过修正估计中不符合实际的短时长活动来提升分类方法的准确率。我们还提出了一种新的性能指标——局部时间偏移度量（Locally Time-Shifted Measure，LTS度量），用于解决状态转换时刻的不确定性问题。我们在模拟数据集以及足球传感器数据的真实应用场景中，采用新型LTS度量评估了该后处理方法的有效性。模拟研究还被用于讨论后处理方法与LTS度量的参数选择问题。
英文摘要：
The past decade has seen an increased interest in human activity recognition based on sensor data. Most often, the sensor data come unannotated, creating the need for fast labelling methods. For assessing the quality of the labelling, an appropriate performance measure has to be chosen. Our main contribution is a novel post-processing method for activity recognition. It improves the accuracy of the classification methods by correcting for unrealistic short activities in the estimate. We also propose a new performance measure, the Locally Time-Shifted Measure (LTS measure), which addresses uncertainty in the times of state changes. The effectiveness of the post-processing method is evaluated, using the novel LTS measure, on the basis of a simulated dataset and a real application on sensor data from football. The simulation study is also used to discuss the choice of the parameters of the post-processing method and the LTS measure.
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