A Comprehensive Review of Automated Data Annotation Techniques in Human Activity Recognition
It tackles the annotation bottleneck in HAR for applications like healthcare and industry, but it is incremental as it reviews existing techniques without introducing new methods.
This paper provides a systematic review of automated data annotation techniques in Human Activity Recognition (HAR), addressing the labor-intensive problem of manual annotation by categorizing existing methods to guide selection for specific scenarios.
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.