期刊论文详细信息
Animal Biotelemetry
Classification of behaviour in housed dairy cows using an accelerometer-based activity monitoring system
Jorge A. Vázquez Diosdado3  Zoe E. Barker2  Holly R. Hodges2  Jonathan R. Amory2  Darren P. Croft4  Nick J. Bell1  Edward A. Codling3 
[1] Royal Veterinary College, Hawkshead Lane, North Mymms AL9 7TA, Hatfield, UK
[2] Writtle College, Chelmsford CM1 3RR, Essex, UK
[3] Department of Mathematical Sciences, University of Essex, Wivenhoe Park, Colchester, CO4 3SQ, UK
[4] Center for Research in Animal Behaviour, College of Life and Environmental Sciences, University of Exeter, Exeter EX4 4QG, UK
关键词: Reality mining;    Tri-axial accelerometer;    Precision livestock farming;    Decision-tree algorithm;    Behavioural classification;   
Others  :  1224657
DOI  :  10.1186/s40317-015-0045-8
 received in 2015-02-17, accepted in 2015-05-25,  发布年份 2015
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【 摘 要 】

Background

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.

Results

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.

Conclusions

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.

【 授权许可】

   
2015 Vázquez Diosdado et al.

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