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Automatic activity logging was recently achieved by combining activity recognition techniques with body area sensor networks. However, collecting labeled data requires a rather high human load and is therefore an obstacle that prevents practical implementation of such systems. There are also cases in which human activity cannot be analyzed by using a simple activity set such as that used with conventional approaches. Therefore, we propose an annotation tool based on an active learning approach. Our tool provides an environment where a huge amount of annotation data can be easily obtained, and the labeled data can be continuously collected by seamlessly linking confirmed and annotated tasks. In addition, the tool allows the user to analyze human activity depending on the purpose by using layered activities. We conducted experiments to evaluate the usefulness of our tool. The experiments showed that our tool was effective for reducing the time needed for labeling and was also effective for improving classifiers.