Exploring deep learning techniques for wild animal behaviour classification using animal-borne accelerometers

Machine learning‐based behaviour classification using acceleration data is a powerful tool in bio‐logging research. Deep learning architectures such as convolutional neural networks (CNN), long short‐term memory (LSTM) and self‐attention mechanisms as well as related training techniques have been extensively studied in human activity recognition. However, they have rarely been used in wild animal studies. The main challenges of acceleration‐based wild animal behaviour classification include data shortages, class imbalance problems, various types of noise in data due to differences in individual behaviour and where the loggers were attached and complexity in data due to complex animal‐specific behaviours, which may have limited the application of deep learning techniques in this area. To overcome these challenges, we explored the effectiveness of techniques for efficient model training: data augmentation, manifold mixup and pre‐training of deep learning models with unlabelled data, ..., , , # Data from: Exploring deep learning techniques for wild animal behaviour classification using animal-borne accelerometers

[https://doi.org/10.5061/dryad.2ngf1vhwk](https://doi.org/10.5061/dryad.2ngf1vhwk)

This repository contains the datasets of two seabird species (streaked shearwaters and black-tailed gulls) used in the following paper (Otsuka et al., 2024).

> Otsuka, R., Yoshimura, N., Tanigaki, K., Koyama, S., Mizutani, Y., Yoda, K., & Maekawa, T. (2024). Exploring deep learning techniques for wild animal behaviour classification using animal-borne accelerometers. *Methods in Ecology and Evolution*.

The paper aimed to classify the behaviour of these two seabird species using tri-axial acceleration data and deep learning. It explored the effectiveness of deep learning models and related training techniques, such as data augmentation.

> ⚠️ **WARNING (2025-07-02)**\
> We found that the data collected using the BMX-055 sensor was likely not sampled consistently at the intended fre...,