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Periodicity intensity of the 24 h Circadian Rhythm in newborn calves show indicators of herd welfare

Rhodes, Victoria ORCID: 0000-0002-6638-5981, Maguire, Maureen, Shetty, Meghana, McAloon, Conor ORCID: 0000-0002-4984-4031 and Smeaton, Alan F. ORCID: 0000-0003-1028-8389 (2022) Periodicity intensity of the 24 h Circadian Rhythm in newborn calves show indicators of herd welfare. Sensors, 22 (15). ISSN 1424-8220

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Abstract

Circadian rhythms are a process of the sleep-wake cycle that regulates the physical, mental and behavioural changes in all living beings with a period of roughly 24 h. Wearable accelerometers are typically used in livestock applications to record animal movement from which we can estimate the activity type. Here, we use the overall movement recorded by accelerometers worn on the necks of newborn calves for a period of 8 weeks. From the movement data, we calculate 24 h periodicity intensities corresponding to circadian rhythms, from a 7-day window that slides through up to 8-weeks of data logging. The strength or intensity of the 24 h periodicity is computed at intervals as the calves become older, which is an indicator of individual calf welfare. We observe that the intensities of these 24 h periodicities for individual calves, derived from movement data, increase and decrease synchronously in a herd of 19 calves. Our results show that external factors affecting the welfare of the herd can be observed by processing and visualising movement data in this way and our method reveals insights that are not observable from movement data alone.

Item Type:Article (Published)
Refereed:Yes
Additional Information:Article number: 5843. Externally hosted supplementary file 1 Doi: 10.6084/m9.figshare.20039486.v1
Uncontrolled Keywords:animal welfare; activity monitoring; wearable sensors; data analytics; chronobiology; periodicity
Subjects:Biological Sciences > Food technology
Computer Science > Artificial intelligence
Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Publisher:MDPI
Official URL:https://doi.org/10.3390/s22155843
Copyright Information:© 2022 The Authors.
Funders:UCD Wellcome Institutional Strategic Support Fund (ref 204844/Z/16/Z), Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289_P2 (Insight SFI Research Centre for Data Analytics), co-funded by the European Regional Development Fund
ID Code:27502
Deposited On:08 Aug 2022 08:58 by Alan Smeaton . Last Modified 14 Mar 2023 15:02

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