A Comprehensive Review of Automated Data Annotation Techniques in Human
  Activity Recognition
Florenc Demrozi Cristian Turetta Fadi Al Machot Graziano Pravadelli Philipp H. Kindt
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.