A threshold-based algorithm for the development of inertial sensor-based systems to perform real-time cow step counting in free-stall barns. (January 2017)

• Record Type: Journal Article
• Authors: Arcidiacono, Claudia; Porto, Simona M.C.; Mancino, Massimo; Cascone, Giovanni
• Is Part Of: Biosystems engineering. Volume 153(2017)
• Page Start: 99
• Page End: 109
• Publication Date: 2017-01
• Subjects: Cow behaviour -- Precision livestock farming -- Dairy farming -- Step counter; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2016.11.003
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : Innovative systems and automated computational procedures, such as those based on computer vision or inertial wearable sensors, have recently been adopted to provide effective and accurate monitoring and analysis of cow behaviour and respond to different issues related to cow health and welfare. In this study, a new and open source algorithm, characterised by a linear computational time, was defined and implemented with the aim to improve real-time monitoring and analysis of walking behaviour of dairy cows. It was applied to a novel inertial sensor-based system composed of low-cost devices, including wearable sensors, open source software, operating with a 4-Hz sampling frequency. The algorithm computed the number of steps of each cow from accelerometer data by making use of statistically defined thresholds. Two vector variables were considered to study the accelerometer signals, i.e., Signal Vector Magnitude and Signal Magnitude Area. Algorithm accuracy was carried out by comparing total error ( E ) and Relative Measurement Error ( RME ), and a sensitivity analysis on the parameters of the computed thresholds was carried out to analyse the variation of the error made by the algorithm. The results showed that the algorithm produced an E equal to 9.5%, and a RME between 2.4% and 4.8%. The sensitivity analysis confirmed that the proposed thresholds provided the minimum errors and that RME is less suitable than E for measuring the accuracy of the step counter. In fact, the underestimated and overestimated numbers of steps counted by the algorithm tended to compensate each other in RME computation. Highlights: The study aimed at analysing cow walking behaviour from accelerometer data. A novel algorithm for cow step counting was developed and validated. The algorithm made use of statistically determined thresholds. Algorithm accuracy and sensitivity analysis on thresholds were carried out. Algorithm was applied to a 4 Hz sampling frequency low-cost inertial-based system.