Applying Deep Learning Models to Action Recognition of Swimming Mice with the Scarcity of Training Data

Ngoc Nguyen, Mera Delimayanti, Bedy Purnama, Kunti Mahmudah, Mamoru Kubo, Makiko Kakikawa, Yoichi Yamada, Kenji Satou

Abstract

Deep learning models have shown their ability to model complicated problems in more efficient ways than other machine learning techniques in many application fields. For human action recognition tasks, the current state-of-the-art models are deep learning models. But they are not well-studied in applying for animal behaviour recognition due to the lack of data required for training these models. Therefore, in this research, we proposed a method to apply deep learning models to recognize the behaviours of a swimming mouse in two mouse forced swim tests with a limited amount of training data. We used deep learning models which are used in human action recognition tasks and fine-tuned them on the largest publicly available mouse behaviour dataset to give the models the knowledge about mouse behaviour recognition tasks. Then we fine-tuned the models one more time using the small amount of data that we have annotated for our swimming mouse behaviour recognition tasks. The good performance of these models in the new tasks proved the efficiency of our approach.

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Paper Citation


in Harvard Style

Nguyen N., Delimayanti M., Purnama B., Mahmudah K., Kubo M., Kakikawa M., Yamada Y. and Satou K. (2019). Applying Deep Learning Models to Action Recognition of Swimming Mice with the Scarcity of Training Data.In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, ISBN 978-989-758-353-7, pages 270-275. DOI: 10.5220/0007567602700275


in Bibtex Style

@conference{bioinformatics19,
author={Ngoc Nguyen and Mera Delimayanti and Bedy Purnama and Kunti Mahmudah and Mamoru Kubo and Makiko Kakikawa and Yoichi Yamada and Kenji Satou},
title={Applying Deep Learning Models to Action Recognition of Swimming Mice with the Scarcity of Training Data},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS,},
year={2019},
pages={270-275},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007567602700275},
isbn={978-989-758-353-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS,
TI - Applying Deep Learning Models to Action Recognition of Swimming Mice with the Scarcity of Training Data
SN - 978-989-758-353-7
AU - Nguyen N.
AU - Delimayanti M.
AU - Purnama B.
AU - Mahmudah K.
AU - Kubo M.
AU - Kakikawa M.
AU - Yamada Y.
AU - Satou K.
PY - 2019
SP - 270
EP - 275
DO - 10.5220/0007567602700275