A pipeline with pre-processing options to detect behaviour from accelerometer data using Machine Learning tested on dairy goats

Animal behaviour is a significant component in the evaluation of animal welfare. Conducting continuous observations of animal behaviour is a time-consuming task and may not be feasible over extended periods for all animals. Thus, new technologies like sensors and cameras can be used to assess individual behaviour continuously. Combined with Artificial Intelligence (AI), accelerometers are promising to continuously and individually detect animal behaviour from the acceleration signals and characteristics of the behaviour. Such devices are commercialised for cattle but they have not been widely developed for small ruminants. Being able to automatically monitor behaviour at an individual scale represents a crucial step towards an objective assessment of animal welfare. This paper aims to present the use of a pipeline called ACT4Behav (Accelerometer-based Classification Tool for identifying Behaviours) involving a supervised classification algorithm for automatically characterising specific animal behaviours using accelerometer data, and to explore the best pre-processing steps for each behaviour. This algorithm is designed to be general-purpose and applicable with different species, behaviours and accelerometers. This paper presents the use of this pipeline with eight indoor-housed goats equipped with ear-mounted accelerometers. Rumination, head in the feeder, standing and lying behaviours were continuously sampled from camera recordings for 11 consecutive hours for each goat using The Observer software. The developed pipeline was used to identify optimal descriptive features and data preparation steps for each prediction model, one for each behaviour. A sensitivity analysis was conducted to assess the impact of the processing techniques and parameter value on the resulting AUC (Area Under the Curve) score, used as the performance score of the models. This analysis allowed the identification of the adequate filtering techniques, time-window segmentations, application of various transformations to raw data, and feature selections for each behaviour. Tuning the data pre-processing for each behaviour enhanced the ability to predict rumination (AUC score=0.800), head in the feeder (AUC score=0.819), lying (AUC score=0.829) and standing (AUC score=0.823) behaviours. When the application of the models on goats that did not participate in the training was tested by training the models on six goats and testing it on the two other goats, the AUC score for the four behaviours decreased (0.644, 0.733, 0.741 and 0.749 respectively for rumination, head in the feeder, lying and standing).