Generic online animal activity recognition on collar tags

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    8 Citations (Scopus)
    84 Downloads (Pure)

    Abstract

    Animal behaviour is a commonly-used and sensitive indicator of animal welfare. Moreover, the behaviour of animals can provide rich information about their environment. For online activity recognition on collar tags of animals, fundamental challenges include: limited energy resources, limited CPU and memory availability, and heterogeneity of animals. In this paper, we propose to tackle these challenges with a framework that employs Multitask Learning for embedded platforms. We train the classifiers with shared training data and a shared feature-representation. We show that Multitask Learning has a significant positive effect on the performance of the classifiers. Furthermore, we compare 7 types of classifiers in terms of resource usage and activity recognition performance on real-world movement data from goats and sheep. A Deep Neural Network could obtain an accuracy of 94% when tested with the data from both species. Our results show that a Deep Neural Network performs the best among the compared classifiers in terms of complexity versus performance. This work supports the development of a robust generic classifier that can run on a small embedded system with good performance, as well as sustain the lifetime of online activity recognition systems.
    Original languageEnglish
    Title of host publicationUbiComp'17
    Subtitle of host publicationProceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers
    Place of PublicationNew York, NY
    PublisherACM Press
    Pages597-606
    Number of pages10
    ISBN (Print)9781450351904
    DOIs
    Publication statusPublished - 2017
    Event2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2017 - Maui, United States
    Duration: 11 Sep 201715 Sep 2017
    http://ubicomp.org/ubicomp2017/

    Conference

    Conference2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2017
    Abbreviated titleUbiComp
    CountryUnited States
    CityMaui
    Period11/09/1715/09/17
    Internet address

      Fingerprint

    Cite this

    Kamminga, J. W., Bisby, H. C., Le, D. V., Meratnia, N., & Havinga, P. J. M. (2017). Generic online animal activity recognition on collar tags. In UbiComp'17: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers (pp. 597-606). New York, NY: ACM Press. https://doi.org/10.1145/3123024.3124407