Research graph
References from A pipeline with pre-processing options to detect behaviour from accelerometer data using Machine Learning tested on dairy goats. Local targets link to admitted publications; unresolved targets remain external evidence.
Opportunities to Apply Precision Livestock Management on Rangelands
2021 · External reference
Window Size Impact in Human Activity Recognition
10.3390/s140406474 · 2014 · External reference
Precision Livestock Farming Technologies for Welfare Management in Intensive Livestock Systems: -EN- Precision Livestock Farming Technologies for Welfare Management in Intensive Livestock Systems -FR- Les Technologies de l'élevage de Précision Appliquées à La Gestion Du Bien-Être Animal Dans Les Systèmes d'élevage Intensif -ES- Tecnologías de Ganadería de Precisión Para La Gestión Del Bienestar En Sistemas de Ganadería Intensiva
10.20506/rst.33.1.2273 · 2014 · External reference
Sensor Data Acquisition and Processing Parameters for Human Activity Classification
10.3390/s140304239 · 2014 · External reference
A Validation of Technologies Monitoring Dairy Cow Feeding, Ruminating, and Lying Behaviors
10.3168/jds.2015-10843 · 2016 · External reference
Observing the Unwatchable through Acceleration Logging of Animal Behavior
10.1186/2050-3385-1-20 · 2013 · External reference
Animal Welfare Management in a Digital World
10.3390/ani10101779 · 2020 · External reference
Analysis of Accelerometer and GPS Data for Cattle Behaviour Identification and Anomalous Events Detection
10.3390/e24030336 · 2022 · External reference
Accelerometer Systems as Tools for Health and Welfare Assessment in Cattle and Pigs – A Review
10.1016/j.beproc.2020.104262 · 2020 · External reference
Time Series FeatuRe Extraction on Basis of Scalable Hypothesis Tests (Tsfresh – A Python Package)
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Evaluation of Proper Sensor Position for Classification of Sheep Behaviour through Accelerometers
10.1016/j.smallrumres.2021.106445 · 2021 · External reference
Sow-Activity Classification from Acceleration Patterns: A Machine Learning Approach
10.1016/j.compag.2013.01.003 · 2013 · External reference
Pre-Processing Effect on the Accuracy of Event-Based Activity Segmentation and Classification through Inertial Sensors
10.3390/s150923095 · 2015 · External reference
A Review of Livestock Monitoring and the Need for Integrated Systems
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Changes in Feeding Behavior as Possible Indicators for the Automatic Monitoring of Health Disorders in Dairy Cows
10.3168/jds.2007-0530 · 2008 · External reference
Behavioural Indicators of Sheep and Goat Welfare in Organic and Conventional Norwegian Farms
10.1080/09064702.2015.1050447 · 2015 · External reference
Permutation Entropy and Information Recovery in Nonlinear Dynamic Economic Time Series
10.3390/econometrics7010010 · 2019 · External reference
Inclusion of Features Derived from a Mixture of Time Window Sizes Improved Classification Accuracy of Machine Learning Algorithms for Sheep Grazing Behaviours
10.1016/j.compag.2020.105857 · 2020 · External reference
Robust Sensor-Orientation-Independent Feature Selection for Animal Activity Recognition on Collar Tags
10.1145/3191747 · 2018 · External reference
Unresolved reference
2020 · External reference
A Dynamic Sliding Window Approach for Activity Recognition
10.1007/978-3-642-22362-4_19 · 2011 · External reference
Classification of Sheep Urination Events Using Accelerometers to Aid Improved Measurements of Livestock Contributions to Nitrous Oxide Emissions
10.1016/j.compag.2018.04.018 · 2018 · External reference
An Insight into Classification with Imbalanced Data: Empirical Results and Current Trends on Using Data Intrinsic Characteristics
10.1016/j.ins.2013.07.007 · 2013 · External reference
A Model for Monitoring the Condition of Young Pigs by Their Drinking Behaviour
10.1016/j.compag.2005.02.014 · 2005 · External reference
Feature Selection and Comparison of Machine Learning Algorithms in Classification of Grazing and Rumination Behaviour in Sheep
10.3390/s18103532 · 2018 · External reference
Data Paper: A Goat Behaviour Dataset Combining Labelled Behaviours and Accelerometer Data for Training Machine Learning Detection Models
10.1016/j.anopes.2025.100095 · 2025 · External reference
ACT4Behav (Accelerometer-based Classification Tool for Identifying Behaviours): A Machine Learning Pipeline with Extensive Pre-Processing and Feature Creation Options
10.5281/zenodo.12624796 · 2024 · External reference
Supplementary Information for A Pipeline with Pre-Processing Options to Detect Behaviour from Accelerometer Data Using Machine Learning Tested on Dairy Goats.
10.5281/zenodo.15148455 · 2025 · External reference
Accelerometers and annotated video recordings of behaviours: a dataset for training behaviour classification models in goats
10.57745/lgzbm1 · 2024 · External reference
A New Method to Measure Behavioural Activity Levels in Dairy Cows
10.1016/s0168-1591(03)00141-2 · 2003 · External reference
Scikit-Learn: Machine Learning in Python
2011 · External reference
Technical Note: Validation of an Ear-Tag Accelerometer Sensor to Determine Rumination, Eating, and Activity Behaviors of Grazing Dairy Cattle
10.3168/jds.2016-12534 · 2018 · External reference
Expert Systems in Parameter Extraction of the ECG Signal
10.1109/iembs.1988.94460 · 1988 · External reference
CatBoost: Unbiased Boosting with Categorical Features
10.48550/arxiv.1706.09516 · 2019 · External reference
Cattle Behaviour Classification from Collar, Halter, and Ear Tag Sensors
10.1016/j.inpa.2017.10.001 · 2018 · External reference
Introducing a feature extraction method and implementing a gradient boosting algorithm to predict behavioral traits in animals
10.24072/pci.animsci.100341 · 2025 · External reference
Evaluation of Pre-Processing Methods for the Prediction of Cattle Behaviour from Accelerometer Data
10.1016/j.compag.2019.104961 · 2019 · External reference
Predicting Livestock Behaviour Using Accelerometers: A Systematic Review of Processing Techniques for Ruminant Behaviour Prediction from Raw Accelerometer Data
10.1016/j.compag.2021.106610 · 2022 · External reference
Evaluation of Three-Dimensional Accelerometers to Monitor and Classify Behavior Patterns in Cattle
10.1016/j.compag.2009.03.002 · 2009 · External reference
Behavior Classification of Goats Using 9-Axis Multi Sensors: The Effect of Imbalanced Datasets on Classification Performance
10.1016/j.compag.2019.105027 · 2019 · External reference
Evaluation of an Ear-Attached Accelerometer for Detecting Estrus Events in Indoor Housed Dairy Cows
10.1016/j.theriogenology.2019.02.038 · 2019 · External reference
Automatic Recognition of Ingestive-Related Behaviors of Dairy Cows Based on Triaxial Acceleration
10.1016/j.inpa.2019.10.004 · 2020 · External reference
Behavior Classification of Cows Fitted with Motion Collars: Decomposing Multi-Class Classification into a Set of Binary Problems
10.1016/j.compag.2016.10.006 · 2016 · External reference
Short Communication: Identification of Diseased Calves by Use of Data from Automatic Milk Feeders
10.3168/jds.s0022-0302(07)71584-9 · 2007 · External reference
Lameness Detection via Leg-Mounted Accelerometers on Dairy Cows on Four Commercial Farms
10.1017/s1751731115000890 · 2015 · External reference
Lameness Affects Cow Feeding But Not Rumination Behavior as Characterized from Sensor Data
2016 · External reference
10.4324/9780203701737
10.4324/9780203701737 · 2019 · External reference
A Combined Offline and Online Algorithm for Real-Time and Long-Term Classification of Sheep Behaviour: Novel Approach for Precision Livestock Farming
10.3390/s19143201 · 2019 · External reference
Evaluation of Sampling Frequency, Window Size and Sensor Position for Classification of Sheep Behaviour
2018 · External reference
Assessing Animal Welfare at the Farm Level: Do We Care Sufficiently about the Individual?
10.7120/09627286.28.1.077 · 2019 · External reference
Technical Note: Validation of Data Loggers for Recording Lying Behavior in Dairy Goats
10.3168/jds.2014-8635 · 2015 · External reference
