# Multimodal sensor data fusion for in-situ classification of animal behavior using accelerometry and GNSS data

arXiv: https://arxiv.org/abs/2206.12078 · HTML: https://mlsanity.com/paper/2206.12078/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 52 references that are in the map.
- First posted on arXiv 24 Jun 2022.

## Builds on (most cited first, 2 of 2)

- [Animal behavior classification via deep learning on embedded systems](https://mlsanity.com/paper/2111.12295/) (2021)
- [Animal Behavior Classification via Accelerometry Data and Recurrent Neural Networks](https://mlsanity.com/paper/2111.12843/) (2021)

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

- [Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges](https://mlsanity.com/paper/2309.07438/) (2023)
- [Animal Behavior Analysis Methods Using Deep Learning: A Survey](https://mlsanity.com/paper/2405.14002/) (2024)
- [A Computer Vision Pipeline for Individual-Level Behavior Analysis: Benchmarking on the Edinburgh Pig Dataset](https://mlsanity.com/paper/2509.12047/) (2025)
- [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 multi-head deep fusion model for recognition of cattle foraging events using sound and movement signals](https://mlsanity.com/paper/2505.10198/) (2025)
- [Multimodal AI Systems for Enhanced Laying Hen Welfare Assessment and Productivity Optimization](https://mlsanity.com/paper/2508.07628/) (2025)
- [Automatic Detection of Signalling Behaviour from Assistance Dogs as they Forecast the Onset of Epileptic Seizures in Humans](https://mlsanity.com/paper/2303.06407/) (2023)
- [A noise-robust acoustic method for recognizing foraging activities of grazing cattle](https://mlsanity.com/paper/2304.14824/) (2023)

## Abstract (arXiv)

In this paper, we examine the use of data from multiple sensing modes, i.e., accelerometry and global navigation satellite system (GNSS), for classifying animal behavior. We extract three new features from the GNSS data, namely, distance from water point, median speed, and median estimated horizontal position error. We combine the information available from the accelerometry and GNSS data via two approaches. The first approach is based on concatenating the features extracted from both sensor data and feeding the concatenated feature vector into a multi-layer perceptron (MLP) classifier. The second approach is based on fusing the posterior probabilities predicted by two MLP classifiers. The input to each classifier is the features extracted from the data of one sensing mode. We evaluate the performance of the developed multimodal animal behavior classification algorithms using two real-world datasets collected via smart cattle collar tags and ear tags. The leave-one-animal-out cross-validation results show that both approaches improve the classification performance appreciably compared with using data of only one sensing mode. This is more notable for the infrequent but important behaviors of walking and drinking. The algorithms developed based on both approaches require little computational and memory resources hence are suitable for implementation on embedded systems of our collar tags and ear tags. However, the multimodal animal behavior classification algorithm based on posterior probability fusion is preferable to the one based on feature concatenation as it delivers better classification accuracy, has less computational and memory complexity, is more robust to sensor data failure, and enjoys better modularity.