Data from: 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, using datasets
from two species of wild seabirds and state‐of‐the‐art deep learning model
architectures. Data augmentation improved the overall model performance
when one of the various techniques (none, scaling, jittering, permutation,
time‐warping and rotation) was randomly applied to each data during
mini‐batch training. Manifold mixup also improved model performance, but
not as much as random data augmentation. Pre‐training with unlabelled data
did not improve model performance. The state‐of‐the‐art deep learning
models, including a model consisting of four CNN layers, an LSTM layer and
a multi‐head attention layer, as well as its modified version with
shortcut connection, showed better performance among other comparative
models. Using only raw acceleration data as inputs, these models
outperformed classic machine learning approaches that used 119 handcrafted
features. Our experiments showed that deep learning techniques are
promising for acceleration‐based behaviour classification of wild animals
and highlighted some challenges (e.g. effective use of unlabelled data).
There is scope for greater exploration of deep learning techniques in wild
animal studies (e.g. advanced data augmentation, multimodal sensor data
use, transfer learning and self‐supervised learning). We hope that this
study will stimulate the development of deep learning techniques for wild
animal behaviour classification using time‐series sensor data. This
abstract is cited from the original article "Exploring deep learning
techniques for wild animal behaviour classification using animal-borne
accelerometers" in Methods in Ecology and Evolution (Otsuka et al.,
2024).Please see README for the details of the datasets.