research-article

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Generic online animal activity recognition on collar tags

Authors:

Jacob W. Kamminga,

Helena C. Bisby,

Nirvana Meratnia,

Paul J. M. HavingaAuthors Info & Claims

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

Pages 597 - 606

https://doi.org/10.1145/3123024.3124407

Published: 11 September 2017 Publication History

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.

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Cited By

Hollevoet ADe Waele TPeralta DTuyttens FDe Poorter EShahid A(2024)Goats on the Move: Evaluating Machine Learning Models for Goat Activity Analysis Using Accelerometer DataAnimals10.3390/ani1413197714:13(1977)Online publication date: 4-Jul-2024
https://doi.org/10.3390/ani14131977
Hussain AAli SJoo MKim H(2024)A Deep Learning Approach for Detecting and Classifying Cat Activity to Monitor and Improve Cat’s Well-Being Using Accelerometer, Gyroscope, and MagnetometerIEEE Sensors Journal10.1109/JSEN.2023.332466524:2(1996-2008)Online publication date: 15-Jan-2024
https://doi.org/10.1109/JSEN.2023.3324665
Fujinami KTakuno RSato IShimmura T(2023)Evaluating Behavior Recognition Pipeline of Laying Hens Using Wearable Inertial SensorsSensors10.3390/s2311507723:11(5077)Online publication date: 25-May-2023
https://doi.org/10.3390/s23115077
Show More Cited By

Index Terms

Generic online animal activity recognition on collar tags
1. Computing methodologies
  1. Machine learning

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Published In

cover image ACM Conferences

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

September 2017

1089 pages

ISBN:9781450351904

DOI:10.1145/3123024

General Chairs:
Seungyon "Claire" Lee
Google
,
Leila Takayama
UC Santa Cruz
,
Khai Truong
University of Toronto

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 11 September 2017

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Nederlandse Organisatie voor Wetenschappelijk Onderzoek

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UbiComp '17

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UbiComp '17: The 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 11 - 15, 2017

Hawaii, Maui

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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UbiComp '24

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The 2024 ACM International Joint Conference on Pervasive and Ubiquitous Computing

October 5 - 9, 2024

Melbourne , VIC , Australia

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Cited By

Hollevoet ADe Waele TPeralta DTuyttens FDe Poorter EShahid A(2024)Goats on the Move: Evaluating Machine Learning Models for Goat Activity Analysis Using Accelerometer DataAnimals10.3390/ani1413197714:13(1977)Online publication date: 4-Jul-2024
https://doi.org/10.3390/ani14131977
Hussain AAli SJoo MKim H(2024)A Deep Learning Approach for Detecting and Classifying Cat Activity to Monitor and Improve Cat’s Well-Being Using Accelerometer, Gyroscope, and MagnetometerIEEE Sensors Journal10.1109/JSEN.2023.332466524:2(1996-2008)Online publication date: 15-Jan-2024
https://doi.org/10.1109/JSEN.2023.3324665
Fujinami KTakuno RSato IShimmura T(2023)Evaluating Behavior Recognition Pipeline of Laying Hens Using Wearable Inertial SensorsSensors10.3390/s2311507723:11(5077)Online publication date: 25-May-2023
https://doi.org/10.3390/s23115077
Campiotti VFinozzi NIrazoqui JCabrera VUngerfeld ROreggioni J(2023)Wearable Device to Monitor Sheep BehaviorIEEE Embedded Systems Letters10.1109/LES.2022.319030515:2(89-92)Online publication date: Jun-2023
https://doi.org/10.1109/LES.2022.3190305
De Waele TShahid APeralta DEerdekens ADeruyck MTuyttens FDe Poorter E(2023)Time-Series-Based Feature Selection and Clustering for Equine Activity Recognition Using AccelerometersIEEE Sensors Journal10.1109/JSEN.2023.326581123:11(11855-11868)Online publication date: 1-Jun-2023
https://doi.org/10.1109/JSEN.2023.3265811
Pan ZChen HZhong WWang AZheng C(2023)A CNN-Based Animal Behavior Recognition Algorithm for Wearable DevicesIEEE Sensors Journal10.1109/JSEN.2023.323901523:5(5156-5164)Online publication date: 1-Mar-2023
https://doi.org/10.1109/JSEN.2023.3239015
Cabrera VDelbuggio ACardoso HFraga DGómez APedemonte MUngerfeld ROreggioni J(2023)Research Platform to Study Sheep Behavior2023 IEEE Conference on AgriFood Electronics (CAFE)10.1109/CAFE58535.2023.10291765(60-64)Online publication date: 25-Sep-2023
https://doi.org/10.1109/CAFE58535.2023.10291765
Garces AObusan GOng JPerez SAlipio MMappatao G(2023)Wi-Fi Based Monitoring of Goat Feeding Behaviour Using Gyro- and Accelerometer Data2023 International Conference on Advanced Technologies for Communications (ATC)10.1109/ATC58710.2023.10318900(350-355)Online publication date: 19-Oct-2023
https://doi.org/10.1109/ATC58710.2023.10318900
Mao AHuang EWang XLiu K(2023)Deep learning-based animal activity recognition with wearable sensorsComputers and Electronics in Agriculture10.1016/j.compag.2023.108043211:COnline publication date: 24-Aug-2023
https://dl.acm.org/doi/10.1016/j.compag.2023.108043
Arablouei RWang LCurrie LYates JAlvarenga FBishop-Hurley G(2023)Animal behavior classification via deep learning on embedded systemsComputers and Electronics in Agriculture10.1016/j.compag.2023.107707207:COnline publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.compag.2023.107707
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