Classification of behaviour in housed dairy cows using an accelerometer-based activity monitoring system

Jorge A Vázquez Diosdado,Darren P Croft,Holly R Hodges,Edward A Codling,Nick J Bell,Jonathan R Amory,Zoe E Barker

doi:10.1186/s40317-015-0045-8

Jorge A Vázquez Diosdado, Darren P Croft + Show 5 more

Open Access

https://doi.org/10.1186/s40317-015-0045-8

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Abstract

Advances in bio-telemetry technology have made it possible to automatically monitor and classify behavioural activities in many animals, including domesticated species such as dairy cows. Automated behavioural classification has the potential to improve health and welfare monitoring processes as part of a Precision Livestock Farming approach. Recent studies have used accelerometers and pedometers to classify behavioural activities in dairy cows, but such approaches often cannot discriminate accurately between biologically important behaviours such as feeding, lying and standing or transition events between lying and standing. In this study we develop a decision-tree algorithm that uses tri-axial accelerometer data from a neck-mounted sensor to both classify biologically important behaviour in dairy cows and to detect transition events between lying and standing. Data were collected from six dairy cows that were monitored continuously for 36 h. Direct visual observations of each cow were used to validate the algorithm. Results show that the decision-tree algorithm is able to accurately classify three types of biologically relevant behaviours: lying (77.42 % sensitivity, 98.63 % precision), standing (88.00 % sensitivity, 55.00 % precision), and feeding (98.78 % sensitivity, 93.10 % precision). Transitions between standing and lying were also detected accurately with an average sensitivity of 96.45 % and an average precision of 87.50 %. The sensitivity and precision of the decision-tree algorithm matches the performance of more computationally intensive algorithms such as hidden Markov models and support vector machines. Biologically important behavioural activities in housed dairy cows can be classified accurately using a simple decision-tree algorithm applied to data collected from a neck-mounted tri-axial accelerometer. The algorithm could form part of a real-time behavioural monitoring system in order to automatically detect dairy cow health and welfare status.

Highlights

Advances in bio-telemetry technology have made it possible to automatically monitor and classify behavioural activities in many animals, including domesticated species such as dairy cows
Over the past decade, there has been a huge increase in the use of remote monitoring devices such as global positioning (GPS) trackers, location sensors, proximity loggers and accelerometers for automated recording of both human and animal behaviour [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]
We develop a decision-tree algorithm that uses tri-axial accelerometer data from a neckmounted sensor to classify biologically important behaviour in dairy cows such as lying, standing and feeding and to detect transition events between lying and standing

Summary

Introduction

Advances in bio-telemetry technology have made it possible to automatically monitor and classify behavioural activities in many animals, including domesticated species such as dairy cows. In this study we develop a decision-tree algorithm that uses tri-axial accelerometer data from a neck-mounted sensor to both classify biologically important behaviour in dairy cows and to detect transition events between lying and standing. An example of this more integrated approach is a recent study by Banerjee et al [18], who developed a method to detect jumps in laying hens based on some of the key features that are used to estimate forces during human vertical jumps [19] Due to their small size and weight, low cost and their potential ability to record high resolution behavioural data for days or months at a time, bio-loggers and biotelemetry devices are increasingly being used to monitor the entire populations of animals in order to infer both individual-level and social behaviour at a range of spatio-temporal scales [17, 20]. In [9], it was not possible to classify feeding behaviour due to the use of a leg-mounted accelerometer

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