Development of a foaling alarm system using an
                    accelerometer

Jung, Youngwook; Chang, Honghee; Yoon, Minjung

doi:10.5187/jast.2022.e75

Cited by 1 publication

(1 citation statement)

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“…Moreover, Support Vector Machine (SVM) algorithms have been utilized to classify grazing, lying, standing, and walking behaviors with 76.9% accuracy [14]. In addition, activity and inactivity can be classified with 98.1% accuracy using a Decision Tree (DT) [14], and horse colic can be detected by attaching a triaxial acceleration sensor to their withers to collect data and measure rolling and sternal recumbency [15]. These studies have shown that it is possible to use various ML techniques to detect labor behaviors by attaching a triaxial acceleration sensor to the hind leg of a mother goat to measure their activity and the amount of time spent lying on their side and stretching their legs when feeling labor pains and to classify labor and non-labor behaviors.…”

Section: Introductionmentioning

confidence: 99%

Development of a Parturition Detection System for Korean Native Black Goats

Kim,

Kim

et al. 2024

Animals

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Korean Native Black Goats deliver mainly during the cold season. However, in winter, there is a high risk of stunted growth and mortality for their newborns. Therefore, we conducted this study to develop a KNBG parturition detection system that detects and provides managers with early notification of the signs of parturition. The KNBG parturition detection system consists of triaxial accelerometers, gateways, a server, and parturition detection alarm terminals. Then, two different data, the labor and non-labor data, were acquired and a Decision Tree algorithm was used to classify them. After classifying the labor and non-labor states, the sum of the labor status data was multiplied by the activity count value to enhance the classification accuracy. Finally, the Labor Pain Index (LPI) was derived. Based on the LPI, the optimal processing time window was determined to be 10 min, and the threshold value for labor classification was determined to be 14 240.92. The parturition detection rate was 82.4%, with 14 out of 17 parturitions successfully detected, and the average parturition detection time was 90.6 min before the actual parturition time of the first kid. The KNBG parturition detection system is expected to reduce the risk of stunted growth and mortality due to hypothermia in KNBG kids by detecting parturition 90.6 min before the parturition of the first kid, with a success rate of 82.4%, enabling parturition nursing.

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Section: Introductionmentioning

confidence: 99%