Animal Behavior Classification via Deep Learning on Embedded Systems. (arXiv:2111.12295v2 [cs.LG] UPDATED)

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 …

arxiv behavior classification deep learning embedded learning lg systems