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Behavioural compass: animal behaviour recognition using magnetometers

BACKGROUND: Animal-borne data loggers today often house several sensors recording simultaneously at high frequency. This offers opportunities to gain fine-scale insights into behaviour from individual-sensor as well as integrated multi-sensor data. In the context of behaviour recognition, even thoug...

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Autores principales: Chakravarty, Pritish, Maalberg, Maiki, Cozzi, Gabriele, Ozgul, Arpat, Aminian, Kamiar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6712732/
https://www.ncbi.nlm.nih.gov/pubmed/31485331
http://dx.doi.org/10.1186/s40462-019-0172-6
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author Chakravarty, Pritish
Maalberg, Maiki
Cozzi, Gabriele
Ozgul, Arpat
Aminian, Kamiar
author_facet Chakravarty, Pritish
Maalberg, Maiki
Cozzi, Gabriele
Ozgul, Arpat
Aminian, Kamiar
author_sort Chakravarty, Pritish
collection PubMed
description BACKGROUND: Animal-borne data loggers today often house several sensors recording simultaneously at high frequency. This offers opportunities to gain fine-scale insights into behaviour from individual-sensor as well as integrated multi-sensor data. In the context of behaviour recognition, even though accelerometers have been used extensively, magnetometers have recently been shown to detect specific behaviours that accelerometers miss. The prevalent constraint of limited training data necessitates the importance of identifying behaviours with high robustness to data from new individuals, and may require fusing data from both these sensors. However, no study yet has developed an end-to-end approach to recognise common animal behaviours such as foraging, locomotion, and resting from magnetometer data in a common classification framework capable of accommodating and comparing data from both sensors. METHODS: We address this by first leveraging magnetometers’ similarity to accelerometers to develop biomechanical descriptors of movement: we use the static component given by sensor tilt with respect to Earth’s local magnetic field to estimate posture, and the dynamic component given by change in sensor tilt with time to characterise movement intensity and periodicity. We use these descriptors within an existing hybrid scheme that combines biomechanics and machine learning to recognise behaviour. We showcase the utility of our method on triaxial magnetometer data collected on ten wild Kalahari meerkats (Suricata suricatta), with annotated video recordings of each individual serving as groundtruth. Finally, we compare our results with accelerometer-based behaviour recognition. RESULTS: The overall recognition accuracy of > 94% obtained with magnetometer data was found to be comparable to that achieved using accelerometer data. Interestingly, higher robustness to inter-individual variability in dynamic behaviour was achieved with the magnetometer, while the accelerometer was better at estimating posture. CONCLUSIONS: Magnetometers were found to accurately identify common behaviours, and were particularly robust to dynamic behaviour recognition. The use of biomechanical considerations to summarise magnetometer data makes the hybrid scheme capable of accommodating data from either or both sensors within the same framework according to each sensor’s strengths. This provides future studies with a method to assess the added benefit of using magnetometers for behaviour recognition. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40462-019-0172-6) contains supplementary material, which is available to authorized users.
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spelling pubmed-67127322019-09-04 Behavioural compass: animal behaviour recognition using magnetometers Chakravarty, Pritish Maalberg, Maiki Cozzi, Gabriele Ozgul, Arpat Aminian, Kamiar Mov Ecol Methodology Article BACKGROUND: Animal-borne data loggers today often house several sensors recording simultaneously at high frequency. This offers opportunities to gain fine-scale insights into behaviour from individual-sensor as well as integrated multi-sensor data. In the context of behaviour recognition, even though accelerometers have been used extensively, magnetometers have recently been shown to detect specific behaviours that accelerometers miss. The prevalent constraint of limited training data necessitates the importance of identifying behaviours with high robustness to data from new individuals, and may require fusing data from both these sensors. However, no study yet has developed an end-to-end approach to recognise common animal behaviours such as foraging, locomotion, and resting from magnetometer data in a common classification framework capable of accommodating and comparing data from both sensors. METHODS: We address this by first leveraging magnetometers’ similarity to accelerometers to develop biomechanical descriptors of movement: we use the static component given by sensor tilt with respect to Earth’s local magnetic field to estimate posture, and the dynamic component given by change in sensor tilt with time to characterise movement intensity and periodicity. We use these descriptors within an existing hybrid scheme that combines biomechanics and machine learning to recognise behaviour. We showcase the utility of our method on triaxial magnetometer data collected on ten wild Kalahari meerkats (Suricata suricatta), with annotated video recordings of each individual serving as groundtruth. Finally, we compare our results with accelerometer-based behaviour recognition. RESULTS: The overall recognition accuracy of > 94% obtained with magnetometer data was found to be comparable to that achieved using accelerometer data. Interestingly, higher robustness to inter-individual variability in dynamic behaviour was achieved with the magnetometer, while the accelerometer was better at estimating posture. CONCLUSIONS: Magnetometers were found to accurately identify common behaviours, and were particularly robust to dynamic behaviour recognition. The use of biomechanical considerations to summarise magnetometer data makes the hybrid scheme capable of accommodating data from either or both sensors within the same framework according to each sensor’s strengths. This provides future studies with a method to assess the added benefit of using magnetometers for behaviour recognition. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40462-019-0172-6) contains supplementary material, which is available to authorized users. BioMed Central 2019-08-27 /pmc/articles/PMC6712732/ /pubmed/31485331 http://dx.doi.org/10.1186/s40462-019-0172-6 Text en © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Methodology Article
Chakravarty, Pritish
Maalberg, Maiki
Cozzi, Gabriele
Ozgul, Arpat
Aminian, Kamiar
Behavioural compass: animal behaviour recognition using magnetometers
title Behavioural compass: animal behaviour recognition using magnetometers
title_full Behavioural compass: animal behaviour recognition using magnetometers
title_fullStr Behavioural compass: animal behaviour recognition using magnetometers
title_full_unstemmed Behavioural compass: animal behaviour recognition using magnetometers
title_short Behavioural compass: animal behaviour recognition using magnetometers
title_sort behavioural compass: animal behaviour recognition using magnetometers
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6712732/
https://www.ncbi.nlm.nih.gov/pubmed/31485331
http://dx.doi.org/10.1186/s40462-019-0172-6
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