Activities per year
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.
|Title of host publication||UbiComp'17|
|Subtitle of host publication||Proceedings 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 Publication||New York, NY|
|Number of pages||10|
|Publication status||Published - 2017|
|Event||2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2017 - Maui, United States|
Duration: 11 Sep 2017 → 15 Sep 2017
|Conference||2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2017|
|Period||11/09/17 → 15/09/17|
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- 1 Participating in a conference, workshop, ...
2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2017
Jacob Wilhelm Kamminga (Participant)11 Sep 2017 → 15 Sep 2017
Activity: Participating in or organising an event › Participating in a conference, workshop, ...
Generic online animal activity recognition on collar tags
Kamminga, J. W. (Creator), Havinga, P. J. M. (Contributor) & Meratnia, N. (Contributor), DANS , 24 Nov 2017
DOI: 10.17026/dans-zp6-fmna, https://www.persistent-identifier.nl/urn:nbn:nl:ui:13-ri-wo2h and one more link, https://dl.acm.org/citation.cfm?id=3124407 (show fewer)
- 24 Citations
- 1 PhD Thesis - Research UT, graduation UT
Hiding in the Deep: Online Animal Activity Recognition using Motion Sensors and Machine LearningKamminga, J. W., 9 Sep 2020, Enschede: University of Twente. 225 p.
Research output: Thesis › PhD Thesis - Research UT, graduation UTOpen AccessFile605 Downloads (Pure)