Accelerometers in collars identify behavioral states in captive African elephants Loxodonta africana

Soltis, Joseph; Wilson, Rory P.; Douglas‐Hamilton, Iain; Vollrath, Fritz; King, Lucy E.; Savage, Anne

doi:10.3354/esr00452

Cited by 38 publications

(48 citation statements)

References 28 publications

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“…Collars are routinely used to house various sensors in both wild and zoo contexts (see citations above), and here, we extend our previous findings from accelerometers embedded in elephant collars (Soltis et al 2012). Our scope extends validations of accelerometer data to detect body orientation (upright versus recumbent) in addition to overall activity levels (Soltis et al 2012), in wild African elephants, as well as zoo-housed elephants (Soltis et al 2012).…”

Section: Introductionsupporting

confidence: 54%

“…In so doing, we provide preliminary data on wild African elephant activity budgets and recumbence and compare the re sults to zoo-housed Disney elephants. These results, combined with those of Soltis et al (2012), provide a robust foundation for the study of African elephant behavior using tri-axial accelerometers.…”

Section: Introductionmentioning

confidence: 98%

“…Three of the subjects were housed at Disney's Animal Kingdom ® , Florida, USA, in a 1.6 ha outdoor exhibit during the day, and various indoor or outdoor enclosures overnight (for details, see Soltis et al 2012, Leighty et al 2009). Data were collected from June to December 2011, and April to September 2013.…”

Section: Study Subjects and Data Collectionmentioning

confidence: 99%

“…Finally, Soulsby (2012) used accelerometers and GPS units in collars on Asian elephants Elephas maximus to measure activity budgets, activity levels associated with specific behaviors, and the correlation between activity level and walking distance. Our group previously attached accelerometers in collars to African elephants at Disney's Animal Kingdom ® , showing that common elephant behaviors (resting, feeding, bathing, and walking) can be differentiated from one another using dynamic acceleration and the periodicity of movement (Soltis et al 2012). We also showed that elephants walk with a different posture dependent on whether they are in a positive or negative emotional state (Wilson et al 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Our scope extends validations of accelerometer data to detect body orientation (upright versus recumbent) in addition to overall activity levels (Soltis et al 2012), in wild African elephants, as well as zoo-housed elephants (Soltis et al 2012).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Accelerometers and simple algorithms identify activity budgets and body orientation in African elephants Loxodonta africana

Soltis¹,

King²,

Vollrath³

et al. 2016

Endang. Species. Res.

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Section: Introductionsupporting

confidence: 54%

Section: Introductionmentioning

confidence: 98%

Section: Study Subjects and Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Accelerometers and simple algorithms identify activity budgets and body orientation in African elephants Loxodonta africana

Soltis¹,

King²,

Vollrath³

et al. 2016

Endang. Species. Res.

Self Cite

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AI‐Enabled Micro Motion Sensors for Revealing the Random Daily Activities of Caged Mice

Liu

Chen

Chan³

et al. 2023

Advanced Intelligent Systems

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More than 120 million mice and rats are used yearly for scientific purposes. While tracking their motion behaviors has been an essential issue for the past decade, present techniques, such as video‐tracking and IMU‐tracking have considerable problems, including requiring a complex setup or relatively large IMU modules that cause stress to the animals. Here, we introduce a wireless IoT motion sensor (i.e., weighing only 2 g) that can be attached and carried by mice to collect motion data continuously for several days. We also introduce a combined segmentation method and an imbalanced learning process that are critical for enabling the recognition of common but random mouse behaviors (i.e., resting, walking, rearing, digging, eating, grooming, drinking water, and scratching) in cages with a macro‐recall of 94.55%. An interactive preprint version of the article can be found at: https://doi.org/10.22541/au.166005321.10787501/v1.

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Identifying animal behaviours from accelerometers: Improving predictive accuracy of machine learning by refining the variables selected, data frequency, and sample duration

Dunford,

Marks,

Wilson

et al. 2024

Ecology and Evolution

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Observing animals in the wild often poses extreme challenges, but animal‐borne accelerometers are increasingly revealing unobservable behaviours. Automated machine learning streamlines behaviour identification from the substantial datasets generated during multi‐animal, long‐term studies; however, the accuracy of such models depends on the qualities of the training data. We examined how data processing influenced the predictive accuracy of random forest (RF) models, leveraging the easily observed domestic cat (Felis catus) as a model organism for terrestrial mammalian behaviours. Nine indoor domestic cats were equipped with collar‐mounted tri‐axial accelerometers, and behaviours were recorded alongside video footage. From this calibrated data, eight datasets were derived with (i) additional descriptive variables, (ii) altered frequencies of acceleration data (40 Hz vs. a mean over 1 s) and (iii) standardised durations of different behaviours. These training datasets were used to generate RF models that were validated against calibrated cat behaviours before identifying the behaviours of five free‐ranging tag‐equipped cats. These predictions were compared to those identified manually to validate the accuracy of the RF models for free‐ranging animal behaviours. RF models accurately predicted the behaviours of indoor domestic cats (F‐measure up to 0.96) with discernible improvements observed with post‐data‐collection processing. Additional variables, standardised durations of behaviours and higher recording frequencies improved model accuracy. However, prediction accuracy varied with different behaviours, where high‐frequency models excelled in identifying fast‐paced behaviours (e.g. locomotion), whereas lower‐frequency models (1 Hz) more accurately identified slower, aperiodic behaviours such as grooming and feeding, particularly when examining free‐ranging cat behaviours. While RF modelling offered a robust means of behaviour identification from accelerometer data, field validations were important to validate model accuracy for free‐ranging individuals. Future studies may benefit from employing similar data processing methods that enhance RF behaviour identification accuracy, with extensive advantages for investigations into ecology, welfare and management of wild animals.

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Accelerometers in collars identify behavioral states in captive African elephants Loxodonta africana

Cited by 38 publications

References 28 publications

Accelerometers and simple algorithms identify activity budgets and body orientation in African elephants Loxodonta africana

Accelerometers and simple algorithms identify activity budgets and body orientation in African elephants Loxodonta africana

AI‐Enabled Micro Motion Sensors for Revealing the Random Daily Activities of Caged Mice

Identifying animal behaviours from accelerometers: Improving predictive accuracy of machine learning by refining the variables selected, data frequency, and sample duration

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