  We study the classification of animal behavior using accelerometry data
through various recurrent neural network (RNN) models. We evaluate the
classification performance and complexity of the considered models, which
feature long short-time memory (LSTM) or gated recurrent unit (GRU)
architectures with varying depths and widths, using four datasets acquired from
cattle via collar or ear tags. We also include two state-of-the-art
convolutional neural network (CNN)-based time-series classification models in
the evaluations. The results show that the RNN-based models can achieve similar
or higher classification accuracy compared with the CNN-based models while
having less computational and memory requirements. We also observe that the
models with GRU architecture generally outperform the ones with LSTM
architecture in terms of classification accuracy despite being less complex. A
single-layer uni-directional GRU model with 64 hidden units appears to offer a
good balance between accuracy and complexity making it suitable for
implementation on edge/embedded devices.
