DOI: 10.5061/dryad.2fr72sb
title: Data from: Behavioural compass: animal behaviour recognition using magnetometers

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 &gt;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.
description: Labelled triaxial
 magnetometer dataPaper: "Behavioural
 compass: animal behaviour recognition using magnetometers" Authors:
 Pritish Chakravarty, Maiki Maalberg, Gabriele Cozzi, Arpat Ozgul, Kamiar
 Aminian ----------------------------------------------------------- A
 guide to accessing and using the labelled, calibrated, windowed (sliding
 window of length two seconds with 50% overlap between successive windows)
 magnetometer data supplied: A .mat file is supplied, which can be opened
 with MATLAB. There is one variable within named
 "sessionWiseMagData_fourBehaviours". It is a cell with eleven
 entries, corresponding to the eleven recording sessions done for this
 study. Let "rs" denote the recording session number (rs = 1, 2,
 3...11). sessionWiseMagData_fourBehaviours{rs} is a cell with four
 entries. Let "b" denote the behaviour number (b = 1,2,3,4 where
 1 corresponds to vigilance, 2 to resting, 3 to foraging, and 4 to
 running). Then sessionWiseMagData_fourBehaviours{rs}{b} contains all the
 two-second bouts of calibrated triaxial magnetometer data recorded for
 behaviour "b" during recording session "rs". Since
 magnetometer data was resampled to 100 Hz, each entry
 sessionWiseMagData_fourBehaviours{rs}{b}{i}, which denotes the i-th
 two-second bout of video-labelled behaviour "b" recorded during
 recording sessions "rs", is a matrix of size 200x3, where 200
 denotes 2s*100 entries/axis/second, and 3 denotes the number of axes of
 the magnetometer). Note that feature computation was done on each of these
 82550 two-second bouts of labelled triaxial magnetometer data for the four
 behaviours of interest.Labelled3DmagData_Chakravarty_et_al_2019_MovEco.rarFeature matricesPaper: "Behavioural compass: animal behaviour recognition using magnetometers" Authors: Pritish Chakravarty, Maiki Maalberg, Gabriele Cozzi, Arpat Ozgul, Kamiar Aminian ----------------------------------------------------------- A note on the feature matrices supplied: - There are two Excel files. "allFeatures.xlsx" contains all nine features (Table 1 in main manuscript) considered in this study. "top3features.xlsx" contains the top three features (feature selection details in Appendix 3, Additional File 1), and is supplied for convenience. - The first row of each Excel file contains the header, arranged as: {feature names, behaviour label, recording session number). Each subsequent row contains feature values computed over a two-second window of triaxial magnetometer data. Both Excel files have the same number of rows, and the same row number refers to the same two-second window of triaxial magnetometer data across both files. - The behaviour labels are as follows. 1: vigilance; 2: resting; 3: foraging; 4: running. - The last column contains the recording session number corresponding to each two-second window of magnetometer data. Data were collected over 11 recording sessions (Table 2 in main manuscript), so the last column contains integer values from 1 to 11.FeatureMatrices_Chakravarty_et_al_2019_MovEco.rar