# Animal Behavior Classification via Accelerometry Data and Recurrent Neural Networks

arXiv: https://arxiv.org/abs/2111.12843 · HTML: https://mlsanity.com/paper/2111.12843/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 24 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (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)
- [A Time Series Forest for Classification and Feature Extraction](https://mlsanity.com/paper/1302.2277/) (2013)

## Built on by (most cited first, 2 of 2)

- [Multimodal sensor data fusion for in-situ classification of animal behavior using accelerometry and GNSS data](https://mlsanity.com/paper/2206.12078/) (2022)
- [In-situ animal behavior classification using knowledge distillation and fixed-point quantization](https://mlsanity.com/paper/2209.04130/) (2022)

## Abstract (arXiv)

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.