# Deep Learning-based Cattle Activity Classification Using Joint Time-frequency Data Representation

arXiv: https://arxiv.org/abs/2011.03381 · HTML: https://mlsanity.com/paper/2011.03381/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2020.

## Built on by (most cited first, 10 of 10)

- [Exploring Edge TPU for Network Intrusion Detection in IoT](https://mlsanity.com/paper/2103.16295/) (2021)
- [Application of deep learning for livestock behaviour recognition: A systematic literature review](https://mlsanity.com/paper/2310.13483/) (2023)
- [OpenAnimalTracks: A Dataset for Animal Track Recognition](https://mlsanity.com/paper/2406.09647/) (2024)
- [Evaluating ROCKET and Catch22 features for calf behaviour classification from accelerometer data using Machine Learning models](https://mlsanity.com/paper/2404.18159/) (2024)
- [Machine Learning Approaches to Predict and Detect Early-Onset of Digital Dermatitis in Dairy Cows using Sensor Data](https://mlsanity.com/paper/2309.10010/) (2023)
- [Temporal Analysis of NetFlow Datasets for Network Intrusion Detection Systems](https://mlsanity.com/paper/2503.04404/) (2025)
- [Exploring Deep Neural Networks on Edge TPU](https://mlsanity.com/paper/2110.08826/) (2021)
- [Improved Sensor-Based Animal Behavior Classification Performance through Conditional Generative Adversarial Network](https://mlsanity.com/paper/2209.03758/) (2022)
- [A Comparison of Deep Learning and Established Methods for Calf Behaviour Monitoring](https://mlsanity.com/paper/2408.13041/) (2024)
- [Toward Optimal Sampling Rate Selection and Unbiased Classification for Precise Animal Activity Recognition](https://mlsanity.com/paper/2604.00517/) (2026)

## Abstract (arXiv)

Automated cattle activity classification allows herders to continuously monitor the health and well-being of livestock, resulting in increased quality and quantity of beef and dairy products. In this paper, a sequential deep neural network is used to develop a behavioural model and to classify cattle behaviour and activities. The key focus of this paper is the exploration of a joint time-frequency domain representation of the sensor data, which is provided as the input to the neural network classifier. Our exploration is based on a real-world data set with over 3 million samples, collected from sensors with a tri-axial accelerometer, magnetometer and gyroscope, attached to collar tags of 10 dairy cows and collected over a one month period. The key results of this paper is that the joint time-frequency data representation, even when used in conjunction with a relatively basic neural network classifier, can outperform the best cattle activity classifiers reported in the literature. With a more systematic exploration of neural network classifier architectures and hyper-parameters, there is potential for even further improvements. Finally, we demonstrate that the time-frequency domain data representation allows us to efficiently trade-off a large reduction of model size and computational complexity for a very minor reduction in classification accuracy. This shows the potential for our classification approach to run on resource-constrained embedded and IoT devices.