Classifying active and inactive states of growing rabbits from accelerometer data using machine learning algorithms
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916313801162752 |
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| author | Mora, Mónica Riaboff, Lucile David, Ingrid Sánchez, Juan Pablo Piles, Miriam |
| author_facet | Mora, Mónica Riaboff, Lucile David, Ingrid Sánchez, Juan Pablo Piles, Miriam |
| contents | This study explores how wearable accelerometers, small devices that measure acceleration, can help monitor the activity of growing rabbits. We equipped 16 rabbits with these devices and filmed them for two weeks. By watching the videos and using a special software we figure out what the rabbits were doing -- things like lying down, eating, moving around, and more. These activitties were grouped into two states: active or inactive. Then, this information along acceleration data was used to teach a computer program to recognize when the rabbits were active or not. This technology offers a reliable way to understand rabbit behavior, which could lead to better management practices in animal production. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_04729 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Classifying active and inactive states of growing rabbits from accelerometer data using machine learning algorithms Mora, Mónica Riaboff, Lucile David, Ingrid Sánchez, Juan Pablo Piles, Miriam Signal Processing This study explores how wearable accelerometers, small devices that measure acceleration, can help monitor the activity of growing rabbits. We equipped 16 rabbits with these devices and filmed them for two weeks. By watching the videos and using a special software we figure out what the rabbits were doing -- things like lying down, eating, moving around, and more. These activitties were grouped into two states: active or inactive. Then, this information along acceleration data was used to teach a computer program to recognize when the rabbits were active or not. This technology offers a reliable way to understand rabbit behavior, which could lead to better management practices in animal production. |
| title | Classifying active and inactive states of growing rabbits from accelerometer data using machine learning algorithms |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2407.04729 |