# Animal behavior classification via deep learning on embedded systems

arXiv: https://arxiv.org/abs/2111.12295 · HTML: https://mlsanity.com/paper/2111.12295/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 41 references that are in the map.
- First posted on arXiv 24 Nov 2021.

## Builds on (most cited first, 3 of 3)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)

## Built on by (most cited first, 9 of 9)

- [Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges](https://mlsanity.com/paper/2309.07438/) (2023)
- [Multimodal sensor data fusion for in-situ classification of animal behavior using accelerometry and GNSS data](https://mlsanity.com/paper/2206.12078/) (2022)
- [Animal Behavior Analysis Methods Using Deep Learning: A Survey](https://mlsanity.com/paper/2405.14002/) (2024)
- [Evaluating ROCKET and Catch22 features for calf behaviour classification from accelerometer data using Machine Learning models](https://mlsanity.com/paper/2404.18159/) (2024)
- [In-situ animal behavior classification using knowledge distillation and fixed-point quantization](https://mlsanity.com/paper/2209.04130/) (2022)
- [CKSP: Cross-species Knowledge Sharing and Preserving for Universal Animal Activity Recognition](https://mlsanity.com/paper/2410.16644/) (2024)
- [A motion-based compression algorithm for resource-constrained video camera traps](https://mlsanity.com/paper/2405.14419/) (2024)
- [A Comparison of Deep Learning and Established Methods for Calf Behaviour Monitoring](https://mlsanity.com/paper/2408.13041/) (2024)
- [Meow-Omni 1: A Multimodal Large Language Model for Feline Ethology](https://mlsanity.com/paper/2605.09152/) (2026)

## Abstract (arXiv)

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.