arXiv subjects

Lucile Riaboff

Publications and source records attributed to Lucile Riaboff.

7 recordsLinked to original sources

arXiv2026

EweAcT: Ewe behaviour aligned to accelerometer data for activity monitoring in extensive grazing systems

Monitoring livestock behaviour under extensive conditions would provide valuable insights to assess animal adaption to environmental perturbations in agroecological systems (e.g., heat waves, parasitism, predator attacks). Animal behaviour can be monitored using accelerometer data collected from neck-collars combined with artificial intelligence models. However, large amounts of accelerometer data aligned with annotated behaviours are necessary to develop accurate models of behaviour prediction. In particular, developing reliable models for extensive systems requires data collected across a wide range of representative conditions. The dataset includes 79 hours of tri-axial accelerometer data aligned with behaviours manually annotated from video recordings for 120 Romane ewes born between 2021 and 2024. The ewes were derived from two divergent genetic lines after three and four generations of selection started 10 years ago: low and high social attractiveness, noted S-and S+, and low and high tolerance towards humans, noted H-and H+. They were reared under the extensive system applied to the Experimental Unit of La Fage (UEF, INRAE, Saint-Jean-et-saint Paul, Aveyron) where 250 sheep were reared exclusively outdoors on 280 hectares of rangeland in southern France. First batch of data was collected on March, June and July 2024 at the UEF under a range of extensive conditions, including sloping pastures and heat-wave periods. Ewes were equipped with accelerometer neck-collars specifically designed for young sheep on pasture. They were grouped on experimental paddocks for 4 to 8 hours and provided with fresh grass and ad libitum access to water. The animals were simultaneously video-recorded using an elevated CCTV camera. Behaviour annotation was carried out using Behavioral Observation Research Interactive Software focusing on the main behaviours on pasture: Grazing, Ruminating, Resting, Moving, and ''Other'', grouping all remaining activities. Annotations and corresponding accelerometer sequences were aligned using Python language, based on a time synchronization procedure. A second batch of data was acquired on November 2025 to supplement the dataset with the moving activity. For that purpose, ewes were equipped with the accelerometer collars and moved on tracks from the housing area to the pastures, corresponding to an approximately 10 minute-walk. The start and end times of the moves for each ewe were used to align the corresponding accelerometer data with the moving activity. These data were then merged with the dataset from the first batch. The resulting dataset is ready to use for applying artificial intelligence models to classify the 5 main behaviours of sheep under extensive grazing systems from accelerometer data.

cs.HC↗

arXiv2026

Genetic determinism of circadian rhythm of feed intake and relation with feed efficiency evaluated in group-housed growing Large White pigs

Background Genetic parameters of feeding behaviours traits from electronic feeding stations in relation to feed efficiency have been widely explored. However, genetic determinism of the circadian rhythm of feed intake throughout the fattening phase in group-housed growing pigs fed ad libitum has never been investigated, despite the well-known relationships between animals' circadian rhythms and the optimization of their metabolism. The objective of this study was to (i) propose three new traits derived from time-frequency approach applied to electronic feeding data from 2,297 Large White pigs that reflect the consistency of circadian feed intake rhythm throughout fattening (so called DayCR) and the precocity of its establishment (so called IndexCR and gCR), and then to (ii) estimate the heritability of those traits and their genetic correlations with residual feed intake using a multiple trait model. Results Results highlighted moderate heritability estimates for the three circadian traits (range h2: [0.24; 0.35]) and high heritability for residual feed intake (0.41). High genetic correlations (range of absolute values: [0.87; 0.98]) among circadian traits suggested that pigs exhibiting a 24-hour periodicity in feed intake on most days of fattening, particularly during the final fattening period, establish their circadian rhythm earlier than the other pigs. The low (range of absolute values: [0.18; 0.27]) but favourable genetic correlations between residual feed intake and circadian traits revealed that animals with a consistent and early 24-hour periodicity of feed intake also tend to be more feed efficient. Conclusions This study proposed to apply time-frequency analysis on longitudinal feeding data to detect 24-hour periodicities in the hourly feed intake pattern across days throughout fattening in growing-pigs. Results suggested that part of the variability observed in the establishment of circadian rhythm is genetically driven, further supporting the feasibility of genetic selection on circadian traits. Considering the well-established biological mechanisms underlying circadian feeding rhythm, selecting animals for their ability to exhibit an early and consistent 24-hour periodicity of feed intake could promote metabolic homeostasis, thereby enhancing animal performance and resilience.

q-bio.PE↗

arXiv2024

A Comparison of Deep Learning and Established Methods for Calf Behaviour Monitoring

In recent years, there has been considerable progress in research on human activity recognition using data from wearable sensors. This technology also has potential in the context of animal welfare in livestock science. In this paper, we report on research on animal activity recognition in support of welfare monitoring. The data comes from collar-mounted accelerometer sensors worn by Holstein and Jersey calves, the objective being to detect changes in behaviour indicating sickness or stress. A key requirement in detecting changes in behaviour is to be able to classify activities into classes, such as drinking, running or walking. In Machine Learning terms, this is a time-series classification task, and in recent years, the Rocket family of methods have emerged as the state-of-the-art in this area. We have over 27 hours of labelled time-series data from 30 calves for our analysis. Using this data as a baseline, we present Rocket's performance on a 6-class classification task. Then, we compare this against the performance of 11 Deep Learning (DL) methods that have been proposed as promising methods for time-series classification. Given the success of DL in related areas, it is reasonable to expect that these methods will perform well here as well. Surprisingly, despite taking care to ensure that the DL methods are configured correctly, none of them match Rocket's performance. A possible explanation for the impressive success of Rocket is that it has the data encoding benefits of DL models in a much simpler classification framework.

cs.LG↗

arXiv2024

Accelerometer-Based Multivariate Time-Series Dataset for Calf Behavior Classification

Getting new insights on pre-weaned calf behavioral adaptation to routine challenges (transport, group relocation, etc.) and diseases (respiratory diseases, diarrhea, etc.) is a promising way to improve calf welfare in dairy farms. A classic approach to automatically monitoring behavior is to equip animals with accelerometers attached to neck collars and to develop machine learning models from accelerometer time-series. However, to be used for model development, data must be equipped with labels. Obtaining these labels requires annotating behaviors from direct observation or videos, a time-consuming and labor-intensive process. To address this challenge, we propose the ActBeCalf (Accelerometer Time-Series for Calf Behaviour classification) dataset: 30 pre-weaned dairy calves (Holstein Friesian and Jersey) were equipped with a 3D-accelerometer sensor attached to a neck-collar from one week of birth for 13 weeks. The calves were simultaneously filmed with a camera in each pen. At the end of the trial, behaviors were manually annotated from the videos using the Behavioral Observation Research Interactive Software (BORIS) by 3 observers using an ethogram with 23 behaviors. ActBeCalf contains 27.4 hours of accelerometer data aligned adequately with calf behaviors. The dataset includes the main behaviors, like lying, standing, walking, and running, and less prominent behaviors, such as sniffing, social interaction, and grooming. Finally, ActBeCalf was used for behavior classification with machine learning models: (i)two classes of behaviors, [active and inactive; model 1] and (ii)four classes of behaviors [running, lying, drinking milk, and 'other' class; model 2] to demonstrate its reliability. We got a balanced accuracy of 92% [model1] and 84% [model2]. ActBeCalf is a comprehensive and ready-to-use dataset for classifying pre-weaned calf behaviour from the acceleration time series.

eess.SP↗

arXiv2024

Classifying active and inactive states of growing rabbits from accelerometer data using machine learning algorithms

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.

eess.SP↗

arXiv2024

Development of a digital tool for monitoring the behaviour of pre-weaned calves using accelerometer neck-collars

Automatic monitoring of calf behaviour is a promising way of assessing animal welfare from their first week on farms. This study aims to (i) develop machine learning models from accelerometer data to classify the main behaviours of pre-weaned calves and (ii) set up a digital tool for monitoring the behaviour of pre-weaned calves from the models' prediction. Thirty pre-weaned calves were equipped with a 3-D accelerometer attached to a neck-collar for two months and filmed simultaneously. The behaviours were annotated, resulting in 27.4 hours of observation aligned with the accelerometer data. The time-series were then split into 3 seconds windows. Two machine learning models were tuned using data from 80% of the calves: (i) a Random Forest model to classify between active and inactive behaviours using a set of 11 hand-craft features [model 1] and (ii) a RidgeClassifierCV model to classify between lying, running, drinking milk and other behaviours using ROCKET features [model 2]. The performance of the models was tested using data from the remaining 20% of the calves. Model 1 achieved a balanced accuracy of 0.92. Model 2 achieved a balanced accuracy of 0.84. Behavioural metrics such as daily activity ratio and episodes of running, lying, drinking milk, and other behaviours expressed over time were deduced from the predictions. All the development was finally embedded into a Python dashboard so that the individual calf metrics could be displayed directly from the raw accelerometer files.

eess.SP↗

arXiv2024

Evaluating ROCKET and Catch22 features for calf behaviour classification from accelerometer data using Machine Learning models

Monitoring calf behaviour continuously would be beneficial to identify routine practices (e.g., weaning, dehorning, etc.) that impact calf welfare in dairy farms. In that regard, accelerometer data collected from neck collars can be used along with Machine Learning models to classify calf behaviour automatically. Hand-crafted features are commonly used in Machine Learning models, while ROCKET and Catch22 features are specifically designed for time-series classification problems in related fields. This study aims to compare the performance of ROCKET and Catch22 features to Hand-Crafted features. 30 Irish Holstein Friesian and Jersey pre-weaned calves were monitored using accelerometer sensors allowing for 27.4 hours of annotated behaviors. Additional time-series were computed from the raw X, Y and Z-axis and split into 3-second time windows. ROCKET, Catch22 and Hand-Crafted features were calculated for each time window, and the dataset was then split into the train, validation and test sets. Each set of features was used to train three Machine Learning models (Random Forest, eXtreme Gradient Boosting, and RidgeClassifierCV) to classify six behaviours indicative of pre-weaned calf welfare (drinking milk, grooming, lying, running, walking and other). Models were tuned with the validation set, and the performance of each feature-model combination was evaluated with the test set. The best performance across the three models was obtained with ROCKET [average balanced accuracy +/- standard deviation] (0.70 +/- 0.07), followed by Catch22 (0.69 +/- 0.05), surpassing Hand-Crafted (0.65 +/- 0.034). The best balanced accuracy (0.77) was obtained with ROCKET and RidgeClassifierCV, followed by Catch22 and Random Forest (0.73). Thus, tailoring these approaches for specific behaviours and contexts will be crucial in advancing precision livestock farming and enhancing animal welfare on a larger scale.

cs.LG↗