  We develop an end-to-end deep-neural-network-based algorithm for classifying
animal behavior using accelerometry data on the embedded system of an
artificial intelligence of things (AIoT) device installed in a wearable collar
tag. The proposed algorithm jointly performs feature extraction and
classification utilizing a set of infinite-impulse-response (IIR) and
finite-impulse-response (FIR) filters together with a multilayer perceptron.
The utilized IIR and FIR filters can be viewed as specific types of recurrent
and convolutional neural network layers, respectively. We evaluate the
performance of the proposed algorithm via two real-world datasets collected
from total eighteen grazing beef cattle using collar tags. The results show
that the proposed algorithm offers good intra- and inter-dataset classification
accuracy and outperforms its closest contenders including two state-of-the-art
convolutional-neural-network-based time-series classification algorithms, which
are significantly more complex. We implement the proposed algorithm on the
embedded system of the utilized collar tags' AIoT device to perform in-situ
classification of animal behavior. We achieve real-time in-situ behavior
inference from accelerometry data without imposing any strain on the available
computational, memory, or energy resources of the embedded system.
