A Knowledge-Driven Tool for Automatic Activity Dataset Annotation

Gorka Azkune, Aitor Almeida, Diego López-de-ipiña, Liming Chen

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

    4 Citations (Scopus)

    Abstract

    Human activity recognition has become a very important research topic, due to its multiple applications in areas such as pervasive computing, surveillance, context-aware computing, ambient assistive living or social robotics. For activity recognition approaches to be properly developed and tested, annotated datasets are a key resource. However, few research works deal with activity annotation methods. In this paper, we describe a knowledge-driven approach to annotate activity datasets automatically. Minimal activity models have to be provided to the tool, which uses a novel algorithm to annotate datasets. Minimal activity models specify action patterns. Those actions are directly linked to sensor activations, which can appear in the dataset in varied orders and with interleaved actions that are not in the pattern itself. The presented algorithm finds those patterns and annotates activities accordingly. Obtained results confirm the reliability and robustness of the approach in several experiments involving noisy and changing activity executions.
    Original languageEnglish
    Title of host publicationIntelligent Systems'2014. Advances in Intelligent Systems and Computing
    Pages593-604
    Volume322
    DOIs
    Publication statusPublished - 2015

    Publication series

    NameIntelligent Systems'2014
    Volume322
    ISSN (Print)2194-5357
    ISSN (Electronic)2194-5365

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