ArticleAnimals : an open access journal from MDPI2021
Machine Learning to Detect Posture and Behavior in Dairy Cows: Information from an Accelerometer on the Animal's Left Flank.
Article in Animals : an open access journal from MDPI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 51 citations in OpenAlex.
- Evaluation of Daily Behavioral Patterns and Locomotor Activity in Holstein Dairy Cattle Under Different Physiological Stages and Management Factors.Animals : an open access journal from MDPI · 2026Article
- STELLAR-CB: Synthetic Temporal LSTM for Livestock Activity Recognition-Cow Behaviour.Veterinary medicine and science · 2026Article
- ASAS-NANP Symposium: Mathematical modeling in animal nutrition: revolutionizing animal farming with artificial intelligence: trends, challenges, and opportunities.Journal of animal science · 2026Review
- Wearable Sensors-Based Intelligent Sensing and Application of Animal Behaviors: A Comprehensive Review.Sensors (Basel, Switzerland) · 2025Review
- Research on Calf Behavior Recognition Based on Improved Lightweight YOLOv8 in Farming Scenarios.Animals : an open access journal from MDPI · 2025Article
- Predictive modeling of cattle calving time emphasizing abnormal and normal cases by using posture analysis.Scientific reports · 2024Article
- Monitoring Multiple Behaviors in Beef Calves Raised in Cow-Calf Contact Systems Using a Machine Learning Approach.Animals : an open access journal from MDPI · 2024Article
- Programming and Setting Up the Object Detection Algorithm YOLO to Determine Feeding Activities of Beef Cattle: A Comparison between YOLOv8m and YOLOv10m.Animals : an open access journal from MDPI · 2024Article
- Design of an intelligent wearable device for real-time cattle health monitoring.Frontiers in robotics and AI · 2024Article
- Uncovering Patterns in Dairy Cow Behaviour: A Deep Learning Approach with Tri-Axial Accelerometer Data.Animals : an open access journal from MDPI · 2023Article
- Cow Behavioural Activities in Extensive Farms: Challenges of Adopting Automatic Monitoring Systems.Sensors (Basel, Switzerland) · 2023Review
- On the Development of a Wearable Animal Monitor.Animals : an open access journal from MDPI · 2022Article
- Applying Machine Learning Algorithms for the Classification of Mink Infected with Aleutian Disease Using Different Data Sources.Animals : an open access journal from MDPI · 2022Article
- High Precision Classification of Resting and Eating Behaviors of Cattle by Using a Collar-Fitted Triaxial Accelerometer Sensor.Sensors (Basel, Switzerland) · 2022Article
- Recognition of Cattle's Feeding Behaviors Using Noseband Pressure Sensor With Machine Learning.Frontiers in veterinary science · 2022Article
- Prediction of Cow Calving in Extensive Livestock Using a New Neck-Mounted Sensorized Wearable Device: A Pilot Study.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
The aim of the present study was to develop a model to identify posture and behavior from data collected by a triaxial accelerometer located on the left flank of dairy cows and evaluate its accuracy and precision. Twelve Italian Red-and-White lactating cows were equipped with an accelerometer and observed on average for 136 ± 29 min per cow by two trained operators as a reference. The acceleration data were grouped in time windows of 8 s overlapping by 33.0%, for a total of 35,133 rows. For each row, 32 different features were extracted and used by machine learning algorithms for the classification of posture and behavior. To build up a predictive model, the dataset was split in training and testing datasets, characterized by 75.0 and 25.0% of the observations, respectively. Four algorithms were tested: Random Forest, K Nearest Neighbors, Extreme Boosting Algorithm (XGB), and Support Vector Machine. The XGB model showed the best accuracy (0.99) and Cohen's kappa (0.99) in predicting posture, whereas the Random Forest model had the highest overall accuracy in predicting behaviors (0.76), showing a balanced accuracy from 0.96 for resting to 0.77 for moving. Overall, very accurate detection of the posture and resting behavior were achieved.
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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.