Animal behaviour analysis with GPS and 3D accelerometers

6th European Conference on Precision Livestock Farming

Andrew Spink, Brian Cresswell, Andrea Kölzsch, Frank van Langevelde, Marjolein Neefjes,

Lucas Noldus, Herman van Oeveren, Herbert Prins, Tamme van der Wal, Nelleke de Weerd &

Willem Frederik de Boer

E-Track Consortium

• Noldus Information Technology BV

• AeroVision BV

• Biotrack Ltd

• Wageningen University

• Leibniz Institute for Zoo and Wildlife Research

• Netherlands Institute of Ecology

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Better and more reliably track and analyse the movements and behaviour of animals under field conditions

• Limited battery life means sample rate has to be low — Smaller and more efficient GPS receivers

• Inefficiencies of data import, formatting, smoothing, etc — Complete integrated solution of software and hardware

• Location is the start, knowing what the animal is doing is more relevant —EGNOS-enhanced GPS to detect animal behaviours

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Position of cow determined by GNSS (GPS)

Ionosphere causes inaccuracies

EGNOS ground station measures difference in actual position and GNSS position

Correction factor transmitted live by Galileo satellite and offline by internet (EDAS)

• European Geo-stationary Navigation Overlay Service

• Accuracy improves from about 10m to 1-2m

• Factors such a tree canopies and deep valleys will still cause inaccuracies (less satellites)

• More accurate tracking opens the possibility to go beyond simple location data to detection of behaviours

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Livestock Experiment

Methods

• Cows equipped with GPS collars and manually observed with Pocket Observer

• GPS data visualized with TrackLab

• Correlation between behaviours & GPS data tested with ANOVA

• Decision tree used to create a model, which was then validated on new data

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Livestock Experiment

• Cow moves slower near water and silage

• Searching behaviour (long segments, high speed) cf foraging behaviour (short segments, low speed)

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Results

Livestock Experiment

Results (raw data)

• Ruminating and Standing are not different

• Foraging, Walking, do differ from Ruminating/Standing

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Turn

angle

°

Livestock Experiment

Results (raw data)• Walking has higher velocity than other behaviours• Foraging & Walking have lower turn angle than

Ruminating and Standing

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Turn

angle

°

Dis

tance (

m)

ForagingRuminating

WalkingStanding

Livestock experiment

0

10

20

30

40

50

60

70

80

90

100

Foraging Ruminating Standing Walking Standing &Ruminating

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% correctly classified

Livestock Experiment

Results – Effects of forest canopy

• Spread of data is less than in open area

• Groups still different

• Correct classification is only 57%

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GPS & Accelerometer data

Methods

• EGNOS GPS collars attached to geese (<2% body weight)

• 3D accelerometers on collars (not just tilt switch)

• Behaviours manually scored with The Observer XT

• Preliminary data – visual comparisons

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GPS & Accelerometer data

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GPS & Accelerometer data

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Behaviours which cannot be distinguished by GPS, have

different accelerometer waveforms

Ongoing research

• TrackLab will be expanded to include import of accelerometer data, data segmentation, analysis of social behaviours

• We are very interested in feedback!

• Combined GPS, accelerometer & other data for detection of behaviours

• Library of behaviours & parameters to detect them

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Ongoing research

• Intelligent geofencing — accurate positions, behaviours as input

• Combination of indoor and outdoor tracking

• Partnership with researchers working with precision livestock

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GPS & Accelerometer data

Conclusions

• GPS data can be used to distinguish a variety of behaviours

• The more accurate the GPS, the more behaviours

• Additional data such as from accelerometers can enable detection of more behaviours— more research needed

• TrackLab is an efficient solution for acquiring, visualizing and analysing tracking data

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E-Track is carried out in the context of the Galileo FP7 R&D programme supervised by the GSA. (nr. 277679-2). For more information, please check

www.etrack-project.eu .

Thank you for your attention.

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