article

Original papers: Modelling and monitoring sows' activity types in farrowing house using acceleration data

Authors:

Cécile Cornou,

Søren Lundbye-Christensen,

Anders Ringgaard KristensenAuthors Info & Claims

Computers and Electronics in Agriculture, Volume 76, Issue 2

Pages 316 - 324

https://doi.org/10.1016/j.compag.2011.02.010

Published: 01 May 2011 Publication History

Abstract

This article suggests a method for classifying sows' activity types performed in farrowing house. Five types of activity are modeled using multivariate dynamic linear models: high active (HA), medium active (MA), lying laterally on one side (L1), lying laterally on the other side (L2) and lying sternally (LS). The classification method is based on a Multi-Process Kalman Filter (MPKF) of class I. The performance of the method is validated using a Test data set. Results of activity classification appear satisfying: 75-100% of series are correctly classified within their activity type. When collapsing activity types into active (HA and MA) vs. passive (L1, L2, LS) categories, results range from 96 to 100%. In a second step, the suggested method is applied on series collected for 19 sows around the onset of farrowing, including 9 sows that received bedding materials (57 sow days in total) and 10 sows that received no bedding material (61 sow days in total). Results indicate that there is a marked (i) increase of active behaviours (HA and MA, p<0.001) and (ii) decrease of lying laterally (L1 and L2) behaviours starting 20-16h before the onset of farrowing; during the last 24h before parturition, the averaged time spent lying laterally in a row decreases and the number of changes of activity types for HA and MA increases. These behavioural changes occur for sows both with and without bedding material, but are more marked when bedding material is provided. Straightforward perspectives for applications of this classification method for monitoring activity types are, e.g. automatic detection of farrowing and detection of health disorders.

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Cited By

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: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.compag.2023.108043
Will MBttner KKaufholz TMller-Graf CSelhorst TKrieter J(2017)Accuracy of a real-time location system in static positions under practical conditionsComputers and Electronics in Agriculture10.1016/j.compag.2017.09.020142:PA(473-484)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1016/j.compag.2017.09.020
Dominiak KKristensen A(2017)Prioritizing alarms from sensor-based detection models in livestock production - A review on model performance and alarm reducing methodsComputers and Electronics in Agriculture10.1016/j.compag.2016.12.008133:C(46-67)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1016/j.compag.2016.12.008
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Published In

cover image Computers and Electronics in Agriculture

Computers and Electronics in Agriculture Volume 76, Issue 2

May, 2011

201 pages

ISSN:0168-1699

Issue’s Table of Contents

Copyright © Elsevier B.V. © 2011.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 May 2011

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Cited By

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: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.compag.2023.108043
Will MBttner KKaufholz TMller-Graf CSelhorst TKrieter J(2017)Accuracy of a real-time location system in static positions under practical conditionsComputers and Electronics in Agriculture10.1016/j.compag.2017.09.020142:PA(473-484)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1016/j.compag.2017.09.020
Dominiak KKristensen A(2017)Prioritizing alarms from sensor-based detection models in livestock production - A review on model performance and alarm reducing methodsComputers and Electronics in Agriculture10.1016/j.compag.2016.12.008133:C(46-67)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1016/j.compag.2016.12.008
Thompson RMatheson SPlötz TEdwards SKyriazakis I(2016)Porcine lie detectorsComputers and Electronics in Agriculture10.1016/j.compag.2016.07.017127:C(521-530)Online publication date: 1-Sep-2016
https://dl.acm.org/doi/10.1016/j.compag.2016.07.017
Kashiha MBahr COtt SMoons CNiewold TTuyttens FBerckmans D(2013)Automatic Monitoring of Pig Activity Using Image Analysis15th International Conference on Advanced Concepts for Intelligent Vision Systems - Volume 819210.5555/2718622.2718679(555-563)Online publication date: 28-Oct-2013
https://dl.acm.org/doi/10.5555/2718622.2718679
Oudshoorn FCornou CHellwing AHansen HMunksgaard LLund PKristensen T(2013)Estimation of grass intake on pasture for dairy cows using tightly and loosely mounted di- and tri-axial accelerometers combined with bite countComputers and Electronics in Agriculture10.1016/j.compag.2013.09.01399:C(227-235)Online publication date: 1-Nov-2013
https://dl.acm.org/doi/10.1016/j.compag.2013.09.013
Cornou CLundbye-Christensen S(2012)Modeling of sows diurnal activity pattern and detection of parturition using acceleration measurementsComputers and Electronics in Agriculture10.1016/j.compag.2011.11.00180(97-104)Online publication date: 1-Jan-2012
https://dl.acm.org/doi/10.1016/j.compag.2011.11.001

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