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.

翻译：过去十年间，基于传感器数据的人体活动识别研究日益受到关注。由于传感器数据通常未经标注，这催生了对快速标注方法的需求。为评估标注质量，需选取合适的性能度量指标。本文的主要贡献在于提出了一种新颖的活动识别后处理方法。该方法通过修正估计中不符合实际的短时活动，提升了分类方法的准确性。此外，我们引入了一种新的性能度量指标——局部时间偏移度量（LTS度量），用以应对状态变化时间存在的不确定性。基于模拟数据集和足球传感器数据的实际应用，我们采用新型LTS度量评估了该后处理方法的有效性。同时，通过仿真研究探讨了后处理方法及LTS度量参数的选取策略。