Analyzing Cattle Activity Patterns with Ear Tag Accelerometer Data
In this study, we equip two breeds of cattle located in tropical and temperate climates with smart ear tags containing triaxial accelerometers to measure their activity levels across different time periods. We produce activity profiles when measured by each of four statistical features, the mean, me...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Animals |
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| Online Access: | https://www.mdpi.com/2076-2615/14/2/301 |
| _version_ | 1797344790323396608 |
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| author | Shuwen Hu Antonio Reverter Reza Arablouei Greg Bishop-Hurley Jody McNally Flavio Alvarenga Aaron Ingham |
| author_facet | Shuwen Hu Antonio Reverter Reza Arablouei Greg Bishop-Hurley Jody McNally Flavio Alvarenga Aaron Ingham |
| author_sort | Shuwen Hu |
| collection | DOAJ |
| description | In this study, we equip two breeds of cattle located in tropical and temperate climates with smart ear tags containing triaxial accelerometers to measure their activity levels across different time periods. We produce activity profiles when measured by each of four statistical features, the mean, median, standard deviation, and median absolute deviation of the Euclidean norm of either unfiltered or high-pass-filtered accelerometer readings over five-minute windows. We then aggregate the values from the 5 min windows into hourly or daily (24 h) totals to produce activity profiles for animals kept in each of the test environments. To gain a better understanding of the variation between the peak and nadir activity levels within a 24 h period, we divide each day into multiple equal-length intervals, which can range from 2 to 96 intervals. We then calculate a statistical measure, called daily differential activity (DDA), by computing the differences in feature values for each interval pair. Our findings demonstrate that patterns within the activity profile are more clearly visualised from readings that have been subject to high-pass filtering and that the median of the acceleration vector norm is the most reliable feature for characterising activity and calculating the DDA measure. The underlying causes for these differences remain elusive and is likely attributable to environmental factors, cattle breeds, or management practices. Activity profiles produced from the standard deviation (a feature routinely applied to the quantification of activity level) showed less uniformity between animals and larger variation in values overall. Assessing activity using ear tag accelerometers holds promise for monitoring animal health and welfare. However, optimal results may only be attainable when true diurnal patterns are detected and accounted for. |
| first_indexed | 2024-03-08T11:08:00Z |
| format | Article |
| id | doaj.art-f7f6ca40544e4d1ab88c6100309840d9 |
| institution | Directory Open Access Journal |
| issn | 2076-2615 |
| language | English |
| last_indexed | 2024-03-08T11:08:00Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Animals |
| spelling | doaj.art-f7f6ca40544e4d1ab88c6100309840d92024-01-26T14:33:09ZengMDPI AGAnimals2076-26152024-01-0114230110.3390/ani14020301Analyzing Cattle Activity Patterns with Ear Tag Accelerometer DataShuwen Hu0Antonio Reverter1Reza Arablouei2Greg Bishop-Hurley3Jody McNally4Flavio Alvarenga5Aaron Ingham6Agriculture and Food, CSIRO, Saint Lucia, QLD 4067, AustraliaAgriculture and Food, CSIRO, Saint Lucia, QLD 4067, AustraliaData61, CSIRO, Pullenvale, QLD 4069, AustraliaAgriculture and Food, CSIRO, Saint Lucia, QLD 4067, AustraliaAgriculture and Food, CSIRO, Saint Lucia, QLD 4067, AustraliaNSW Department of Primary Industries, Armidale, NSW 2350, AustraliaAgriculture and Food, CSIRO, Saint Lucia, QLD 4067, AustraliaIn this study, we equip two breeds of cattle located in tropical and temperate climates with smart ear tags containing triaxial accelerometers to measure their activity levels across different time periods. We produce activity profiles when measured by each of four statistical features, the mean, median, standard deviation, and median absolute deviation of the Euclidean norm of either unfiltered or high-pass-filtered accelerometer readings over five-minute windows. We then aggregate the values from the 5 min windows into hourly or daily (24 h) totals to produce activity profiles for animals kept in each of the test environments. To gain a better understanding of the variation between the peak and nadir activity levels within a 24 h period, we divide each day into multiple equal-length intervals, which can range from 2 to 96 intervals. We then calculate a statistical measure, called daily differential activity (DDA), by computing the differences in feature values for each interval pair. Our findings demonstrate that patterns within the activity profile are more clearly visualised from readings that have been subject to high-pass filtering and that the median of the acceleration vector norm is the most reliable feature for characterising activity and calculating the DDA measure. The underlying causes for these differences remain elusive and is likely attributable to environmental factors, cattle breeds, or management practices. Activity profiles produced from the standard deviation (a feature routinely applied to the quantification of activity level) showed less uniformity between animals and larger variation in values overall. Assessing activity using ear tag accelerometers holds promise for monitoring animal health and welfare. However, optimal results may only be attainable when true diurnal patterns are detected and accounted for.https://www.mdpi.com/2076-2615/14/2/301accelerometer datacattle diurnal activitydaily differential activityanimal welfarewearable sensor |
| spellingShingle | Shuwen Hu Antonio Reverter Reza Arablouei Greg Bishop-Hurley Jody McNally Flavio Alvarenga Aaron Ingham Analyzing Cattle Activity Patterns with Ear Tag Accelerometer Data Animals accelerometer data cattle diurnal activity daily differential activity animal welfare wearable sensor |
| title | Analyzing Cattle Activity Patterns with Ear Tag Accelerometer Data |
| title_full | Analyzing Cattle Activity Patterns with Ear Tag Accelerometer Data |
| title_fullStr | Analyzing Cattle Activity Patterns with Ear Tag Accelerometer Data |
| title_full_unstemmed | Analyzing Cattle Activity Patterns with Ear Tag Accelerometer Data |
| title_short | Analyzing Cattle Activity Patterns with Ear Tag Accelerometer Data |
| title_sort | analyzing cattle activity patterns with ear tag accelerometer data |
| topic | accelerometer data cattle diurnal activity daily differential activity animal welfare wearable sensor |
| url | https://www.mdpi.com/2076-2615/14/2/301 |
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