|
|
|
|
|
| AURASOPON, APINAN. |
| Time periods of walking-grazing, standing and lyingof cattle’s life can be used to predict their health. However, famer cannot observe those in all the time. Therefore, this paper proposes a simple technique to classify the cattle behaviors by using the magnitude and the variance of accelerometer output signal. There are two steps of algorithm detection, the first step employed the magnitude of each axis for classifying the cattle behaviors into two groups: 1) walking-grazing and standing and 2) lying. After that, the second step used the variance of Y-axis to notify between walking-grazing and standing behaviors. The classification results were inform time periods of each behavior and tested with two cattle. The measured precise times of each behavior... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Cattle behaviors; Accelerometer; Variance; Decision tree and activity monitoring. |
| Ano: 2015 |
URL: http://www.cigrjournal.org/index.php/Ejounral/article/view/3422 |
| |
|
|
| AURASOPON, APINAN. |
| This paper proposes a method for classifying the cattle behaviors. An embedded accelerometer system has attached to the cow’s neck. Dynamic Time Warping (DTW) is applied to measure the similarity between acceleration data corresponding to the cow movements and the templates collected from the acceleration data corresponding to the cow behaviors. The results of these processes are the sets of accumulated distances whose minimum value is used to select a behavioral model. Two cows used in the experiment, the accuracy of classification was measured. The results show that the accuracy of the proposed system is more than 90 percentages for all behavioral models. Moreover, the three-axis acceleration data combined before sending through the wireless network to... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Cattle behaviors; Embedded accelerometer system; Dynamic Time Warping. |
| Ano: 2016 |
URL: http://www.cigrjournal.org/index.php/Ejounral/article/view/4046 |
| |
|
|
|
