Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
ALT pedometer-New sensor-aided measurement system for improvement in oestrus detection
Computers and Electronics in Agriculture
An automated sensor-based method of simple behavioural classification of sheep in extensive systems
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Modelling the drinking patterns of young pigs using a state space model
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Sows' activity classification device using acceleration data - A resource constrained approach
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
Sow-activity classification from acceleration patterns: A machine learning approach
Computers and Electronics in Agriculture
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Automatic monitoring of animal behavior in livestock production opens up possibilities for on-line monitoring of, among others, oestrus, health disorders, and animal welfare in general. The aim of this study is to use time series of acceleration measurements in order to automatically classify activity types performed by group-housed sows. Extracts of series collected for 11 sows are associated with 5 activity types: feeding (FE), rooting (RO), walking (WA), lying sternally (LS) and lying laterally (LL). A total of 24h of three-dimensional series is used. One univariate model and four multivariate models are used to describe all five activity types. Three multivariate models differ in their variance/covariance structure; a fourth alternative multivariate model (MU) simply combines the 3-axes of the univariate model, assuming independence. For each model, the activity-specific parameters are estimated using the EM algorithm. The classification method, based on a Multi-Process Kalman Filter provides posterior probabilities for each of the 5 activities, for a given series. For the univariate model, LL is the activity which is best recognized by the 3-axes; FE, RO and WA are best recognized by one particular axis; LS is poorest recognized. The average results are improved by using all four types of multivariate models. The percentages of activity recognition are similar among the multivariate models. By grouping the activity types into active (FE, RO, WA) vs. passive (LS, LL) categories, the method allows to correctly classify 96% of the active category and 94% of the passive category.