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Validation of a pedometer algorithm as a tool for evaluation of locomotor behaviour in dairy Mediterranean buffalo

Published online by Cambridge University Press:  20 November 2017

Luigi D'Andrea
Affiliation:
Department of Veterinary Medicine and Animal Productions, University of Napoli ‘Federico II,’ Via Delpino 1, 80137 Napoli, Italy
Jacopo Guccione*
Affiliation:
Department of Veterinary Medicine and Animal Productions, University of Napoli ‘Federico II,’ Via Delpino 1, 80137 Napoli, Italy
Maher Alsaaod
Affiliation:
Clinic for Ruminants, Department of Clinical Veterinary Medicine, Vetsuisse-Faculty, University of Bern, Bern 3001, Switzerland
Ramona Deiss
Affiliation:
Clinic for Ruminants, Department of Clinical Veterinary Medicine, Vetsuisse-Faculty, University of Bern, Bern 3001, Switzerland
Antonio Di Loria
Affiliation:
Department of Experimental and Clinical Medicine, University of Magna Graecia of Catanzaro, 88100 Catanzaro, Italy
Adrian Steiner
Affiliation:
Clinic for Ruminants, Department of Clinical Veterinary Medicine, Vetsuisse-Faculty, University of Bern, Bern 3001, Switzerland
Paolo Ciaramella
Affiliation:
Department of Veterinary Medicine and Animal Productions, University of Napoli ‘Federico II,’ Via Delpino 1, 80137 Napoli, Italy
*
*For correspondence; e-mail: [email protected]

Abstract

This research communication validates an algorithm to monitor natural occurrence of locomotor behaviours in dairy Mediterranean buffalo based on the output of a 3-dimensional accelerometer (RumiWatch®, pedometer). Several characteristics of the locomotor behaviour were detected with a very high (up-right, lying and standing time) or high degree of correlation (walking time and number of strides) and a low mean difference with the video recording. The proportion of correctly detected events exceeded 99 % for the following variables: stand up and lie down events, as well as number of lying, standing or walking bouts. The mean relative measurement error was less than 10 % for the variables: lying, standing, up-right times and number of strides as compared with gold standard. This new algorithm may represent the base for a future early and real-time disease warning system aiming to gain higher health standard in these ruminants.

Type
Research Article
Copyright
Copyright © Hannah Research Foundation 2017 

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References

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