Database generation to identify cow’s movements for detecting estrus and lameness

Muñoz, Carlos; Huircan, Juan Ignacio; Huenupán, Fernando; Pinilla, Diego

doi:10.1109/ica-acca56767.2022.10006230

Cited by 1 publication

(1 citation statement)

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“…Another problem with using raw data is that the IMUs provide this information with different scaling, so if you change from an IMU manufactured by one supplier to one manufactured by a different supplier, the calibration model changes because the parameters obtained from the classifier depend on the type of data used for training. This is relevant since product changes due to the technological obsolescence of the IMUs can lead to the need to collect mount signals for training again, which is a long and complex process [ 17 ]. However, Liu et al [ 18 ] proposes a processing stage for the measured signals that consist of obtaining modules from the raw data, followed by a classification system based on neural networks.…”

Section: Introductionmentioning

confidence: 99%

IMU Auto-Calibration Based on Quaternion Kalman Filter to Identify Movements of Dairy Cows

Muñoz-Poblete,

González-Aguirre,

Bishop

et al. 2024

Sensors

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This work is focused on developing a self-calibration algorithm for an orientation estimation of cattle movements based on a quaternion Kalman filter. The accelerometer signals in the earth’s frame provide more information to confirm that the cow is performing a jump to mount another cow. To obtain the measurements in the earth’s frame, we propose a self-calibration method based on a strapdown inertial navigation system (SINS), which does not require intervention by the user once deployed in the field. The self-calibration algorithm uses a quaternion-based Kalman filter to predict the angular orientation with bias correction, and update it based on the measurements of accelerometers and magnetometers. The paper also depicts an alternate update to adjust the inclination using only the accelerometer measurements. We conducted experiments to compare the accuracy of the orientation estimation when the body moves similarly to cow mount movements. The comparison is between the proposed self-calibration algorithm with the IvenSense MPU9250 and Bosch BNO055 and the quaternion attitude estimation provided in the BNO055. The auto-calibrating algorithm presents a mean error of 0.149 rads with a mean consumption of 308.5 mW, and the Bosch algorithm shows an average error of 0.139 rads with a mean consumption of 307.5 mW. When we executed this algorithm in an MPU9250, the average error was 0.077 rads, and the mean consumption was 277.7 mW.

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Section: Introductionmentioning

confidence: 99%