Unsupervised Domain Adaptation for Human Activity Recognition

Paolo Barbosa, Kemilly Dearo Garcia, Joao Mendes-Moreira

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    Abstract

    Human Activity Recognition has been primarily investigated as a machine learning classification task forcing it to handle with two main limitations. First, it must assume that the testing data has an equal distribution with the training sample. However, the inherent structure of an activity recognition systems is fertile in distribution changes over time, for instance, a specific person can perform physical activities differently from others, and even sensors are prone to misfunction. Secondly, to model the pattern of activities carried out by each user, a significant amount of data is needed. This is impractical especially in the actual era of Big Data with effortless access to public repositories. In order to deal with these problems, this paper investigates the use of Transfer Learning, specifically Unsupervised Domain Adaptation, within human activity recognition systems. The yielded experiment results reveal a useful transfer of knowledge and more importantly the convenience of transfer learning within human activity recognition. Apart from the delineated experiments, our work also contributes to the field of transfer learning in general through an exhaustive survey on transfer learning for human activity recognition based on wearables.
    Original languageEnglish
    Title of host publicationIntelligent Data Engineering and Automated Learning
    Subtitle of host publicationIDEAL 2018
    PublisherSpringer
    Pages623-630
    ISBN (Electronic)978-3-030-03493-1
    ISBN (Print)978-3-030-03492-4
    Publication statusPublished - 21 Nov 2018

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer, Cham
    Volume11314
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

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  • Cite this

    Barbosa, P., Dearo Garcia, K., & Mendes-Moreira, J. (2018). Unsupervised Domain Adaptation for Human Activity Recognition. In Intelligent Data Engineering and Automated Learning: IDEAL 2018 (pp. 623-630). (Lecture Notes in Computer Science; Vol. 11314). Springer.