Behavioral classification of data from collars containing motion sensors in grazing cattle

González, L. A.; Bishop-Hurley, Greg; Handcock, R.N.; Crossman, Chris

doi:10.1016/j.compag.2014.10.018

Cited by 146 publications

(109 citation statements)

References 22 publications

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“…However, the transmission demands high bandwidth which dramatically reduces the precious battery life of a collar tag due to the high energy consumption of radios. Recent studies acknowledge the potential of collaring applications and have evaluated offline activity recognition of cows [7,9,14,25,48], sheep [24,46], and vultures [31]. Smith et al [44] studied features in cattle behavior models, using a greedy search to identify feature subsets that were most effective in classifying activities of steers.…”

Section: Related Workmentioning

confidence: 99%

Robust Sensor-Orientation-Independent Feature Selection for Animal Activity Recognition on Collar Tags

Kamminga

Meijers

et al. 2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Fundamental challenges faced by real-time animal activity recognition include variation in motion data due to changing sensor orientations, numerous features, and energy and processing constraints of animal tags. This paper aims at finding small optimal feature sets that are lightweight and robust to the sensor's orientation. Our approach comprises four main steps. First, 3D feature vectors are selected since they are theoretically independent of orientation. Second, the least interesting features are suppressed to speed up computation and increase robustness against overfitting. Third, the features are further selected through an embedded method, which selects features through simultaneous feature selection and classification. Finally, feature sets are optimized through 10-fold cross-validation. We collected real-world data through multiple sensors around the neck of five goats. The results show that activities can be accurately recognized using only accelerometer data and a few lightweight features. Additionally, we show that the performance is robust to sensor orientation and position. A simple Naive Bayes classifier using only a single feature achieved an accuracy of 94 % with our empirical dataset. Moreover, our optimal feature set yielded an average of 94 % accuracy when applied with six other classifiers. This work supports embedded, real-time, energy-efficient, and robust activity recognition for animals.

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Section: Related Workmentioning

confidence: 99%

Robust Sensor-Orientation-Independent Feature Selection for Animal Activity Recognition on Collar Tags

Kamminga

Meijers

et al. 2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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“…After training, the classifier can classify unlabeled raw data-samples into the learned activity categories. Recently, various studies utilized IMUs for AAR regarding: wildlife [3][4][5][6][7][8][9], livestock [1,2,[10][11][12][13][14][15][16][17][18], and pets [19][20][21].…”

Section: Discussionmentioning

confidence: 99%

Horsing Around—A Dataset Comprising Horse Movement

Kamminga

Janßen

Meratnia

et al. 2019

Data

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Movement data were collected at a riding stable over seven days. The dataset comprises data from 18 individual horses and ponies with 1.2 million 2-s data samples, of which 93,303 samples have been tagged with labels (labeled data). Data from 11 subjects were labeled. The data from six subjects and six activities were labeled more extensively. Data were collected during horse riding sessions and when the horses freely roamed the pasture over seven days. Sensor devices were attached to a collar that was positioned around the neck of horses. The orientation of the sensor devices was not strictly fixed. The sensors devices contained a three-axis accelerometer, gyroscope, and magnetometer and were sampled at 100 Hz.

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“…Today, there are readily-available sensors and Precision Livestock Farming (PLF) tools that offer the possibility to continuously monitor animal behaviour. Accelerometers can record gross activities such as walking, eating, lying down, positioning systems can connect an animal positions to specific resources and thus infer its putative activity, and image analysis can provide further input (González et al, 2015;Andriamandroso et al, 2017;Barwick et al, 2018). As argued by Dawkins et al (2012), the issue is not about collecting data but how to process it.…”

Section: Introductionmentioning

confidence: 99%

Machine learning to detect behavioural anomalies in dairy cows under subacute ruminal acidosis

Wagner

Antoine

Mialon

et al. 2020

Computers and Electronics in Agriculture

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et al.. Machine learning to detect behavioural anomalies in dairy cows under subacute ruminal acidosis. A B S T R A C TSickness behaviour is characterised by a lethargic state during which the animal reduces its activity, sleeps more and at times when normally awake, reduces its feed and water intake, and interacts less with its environment. Subtle modifications in behaviour can materialise just before clinical signs of a disease. Recent sensor developments enable continuous monitoring of animal behaviour, but the shift to abnormal animal activity remains difficult to detect. We explored the use of Machine Learning (ML) to detect abnormal behaviour from continuous monitoring. We submitted 14 cows (Bos taurus) to Sub-Acute Ruminal Acidosis (SARA), a disease known to induce changes in behaviour. Another 14 control cows were not submitted to SARA. We used a ruminal bolus to monitor pH and detect when a cow experienced SARA. We used a positioning system to infer an animal's activity based on its position in relation to specific elements in the barn (feeder, resting area, and alleys). We tested several ML algorithms: K Nearest Neighbours for Regression (KNNR); Decision Tree for Regression (DTR); MultiLayer Perceptron (MLP); Long Short-Term Memory (LSTM); and an algorithm where activity is assumed to be similar from one day to the next. First, we developed ML models to predict activity on a given day from the previous 24 h, considering all cows together. Then, we calculated the error between observed and predicted values for a given cow. Finally, we compared the error to a threshold chosen to optimise the distinction between normal and abnormal values. KNNR performed best, detecting 83% of SARA cases (true-positives), but it also produced 66% of false-positives, which limits its use in practice. In conclusion, ML can help detect anomalies in behaviour. Further improvements could probably be obtained by applying ML on very large datasets at animal rather than group level.

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Behavioral classification of data from collars containing motion sensors in grazing cattle

Cited by 146 publications

References 22 publications

Robust Sensor-Orientation-Independent Feature Selection for Animal Activity Recognition on Collar Tags

Robust Sensor-Orientation-Independent Feature Selection for Animal Activity Recognition on Collar Tags

Horsing Around—A Dataset Comprising Horse Movement

Machine learning to detect behavioural anomalies in dairy cows under subacute ruminal acidosis

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