Human Activity Recognition (HAR) has become one of the leading research topics of the last decade. As sensing technologies have matured and their economic costs have declined, a host of novel applications, e.g., in healthcare, industry, sports, and daily life activities have become popular. The design of HAR systems requires different time-consuming processing steps, such as data collection, annotation, and model training and optimization. In particular, data annotation represents the most labor-intensive and cumbersome step in HAR, since it requires extensive and detailed manual work from human annotators. Therefore, different methodologies concerning the automation of the annotation procedure in HAR have been proposed. The annotation problem occurs in different notions and scenarios, which all require individual solutions. In this paper, we provide the first systematic review on data annotation techniques for HAR. By grouping existing approaches into classes and providing a taxonomy, our goal is to support the decision on which techniques can be beneficially used in a given scenario.

翻译：人体活动识别已成为过去十年间领先的研究课题之一。随着传感技术的成熟及其经济成本的降低，一系列新颖应用（例如在医疗保健、工业、体育和日常生活活动领域）已变得普及。设计人体活动识别系统需要不同耗时处理步骤，如数据收集、标注、模型训练与优化。其中，数据标注是人体活动识别中最劳动密集且最繁琐的环节，因为它需要人工标注者进行大量细致的体力劳动。因此，针对人体活动识别中标注过程的自动化，已提出多种不同方法论。标注问题以不同概念和场景出现，且均需个性化解决方案。本文首次对人体活动识别中的数据标注技术进行了系统性综述。通过将现有方法归类并建立分类体系，我们旨在支持在给定场景下如何有效选用技术的决策。