ArticleSensors (Basel, Switzerland)2023
Machine Learning-Based Sensor Data Fusion for Animal Monitoring: Scoping Review.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 citations in OpenAlex.
- Analyzing the enhancement of CNN-YOLO and transformer based architectures for real-time animal detection in complex ecological environments.Scientific reports · 2025Pooled it
- Six decades of animal accelerometry: trends, applications, and future directions.Movement ecology · 2026Review
- ASAS-NANP Symposium: Mathematical modeling in animal nutrition: revolutionizing animal farming with artificial intelligence: trends, challenges, and opportunities.Journal of animal science · 2026Review
- Generating Accurate Activity Patterns for Cattle Farm Management Using MCMC Simulation of Multiple-Sensor Data System.Sensors (Basel, Switzerland) · 2025Article
- Holstein-Friesian re-identification using multiple cameras and self-supervision on a working farm.Computers and electronics in agriculture · 2025Article
- A Real-Time Vision-Based Adaptive Follow Treadmill for Animal Gait Analysis.Sensors (Basel, Switzerland) · 2025Article
- Multi-Modality Sheep Face Recognition Based on Deep Learning.Animals : an open access journal from MDPI · 2025Article
- CFR-YOLO: A Novel Cow Face Detection Network Based on YOLOv7 Improvement.Sensors (Basel, Switzerland) · 2025Article
- Integrating multi-modal data fusion approaches for analysis of dairy cattle vocalizations.Frontiers in veterinary science · 2025Article
- The roles of patient-derived xenograft models and artificial intelligence toward precision medicine.MedComm · 2024Review
- Comparison of Machine Learning Tree-Based Algorithms to Predict Future Paratuberculosis ELISA Results Using Repeat Milk Tests.Animals : an open access journal from MDPI · 2024Article
- Analyzing Cattle Activity Patterns with Ear Tag Accelerometer Data.Animals : an open access journal from MDPI · 2024Article
- Automated monitoring of brush use in dairy cattle.PloS one · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of technology, such as the Internet of Things and artificial intelligence, has significantly advanced many fields of study. Animal research is no exception, as these technologies have enabled data collection through various sensing devices. Advanced computer systems equipped with artificial intelligence capabilities can process these data, allowing researchers to identify significant behaviors related to the detection of illnesses, discerning the emotional state of the animals, and even recognizing individual animal identities. This review includes articles in the English language published between 2011 and 2022. A total of 263 articles were retrieved, and after applying inclusion criteria, only 23 were deemed eligible for analysis. Sensor fusion algorithms were categorized into three levels: Raw or low (26%), Feature or medium (39%), and Decision or high (34%). Most articles focused on posture and activity detection, and the target species were primarily cows (32%) and horses (12%) in the three levels of fusion. The accelerometer was present at all levels. The findings indicate that the study of sensor fusion applied to animals is still in its early stages and has yet to be fully explored. There is an opportunity to research the use of sensor fusion for combining movement data with biometric sensors to develop animal welfare applications. Overall, the integration of sensor fusion and machine learning algorithms can provide a more in-depth understanding of animal behavior and contribute to better animal welfare, production efficiency, and conservation efforts.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.