Animal behavior classification via deep learning on embedded systems

TL;DR

This paper presents a novel deep learning algorithm for classifying animal behavior using accelerometry data on embedded AIoT devices, achieving high accuracy and real-time inference without resource strain.

Contribution

The paper introduces an end-to-end deep neural network combining IIR and FIR filters with a multilayer perceptron for efficient in-situ animal behavior classification on embedded systems.

Findings

High intra- and inter-dataset classification accuracy

Outperforms state-of-the-art CNN-based classifiers

Achieves real-time inference without resource strain

Abstract

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.