An investigation of cow feeding behavior using motion sensors

Bishop-Hurley, Greg; Henry, D. A.; Smith, Daniel; Dutta, Ritaban; Hills, James; Rawnsley, RP; Hellicar, Andrew; Timms, Greg; Morshed, Ahsan; Rahman, Ashfaqur; D’Este, Claire; Shu, Yanfeng

doi:10.1109/i2mtc.2014.6860952

Cited by 17 publications

(13 citation statements)

References 12 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%

“…Previous research has shown that the physical behavior of animals can be an early indicator of diseases, pain, and heat-stress. It also provides information about social interaction within a herd [7]. The behavior of groups of animals can even be an indicator of the occurrence of environmental events such as forest fires [39], poaching activities [3], and environmental problems [34].…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Introductionmentioning

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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“…Using cattle senor technology and the multi-classifier pattern recognition from this study could provide a novel approach to identifying and monitoring important behavioural changes with respect to optimizing supplementary feed levels at an individual cow basis in pasture based dairy systems [1][2][3][4][5][6][7][8].…”

Section: Motivationmentioning

confidence: 99%

Cattle behaviour classification using 3-axis collar sensor and multi-classifier pattern recognition

Dutta

Smith

Rawnsley

et al. 2014

IEEE SENSORS 2014 Proceedings

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In this paper supervised machine learning techniques based multi-classifier pattern recognition system was developed and applied to classify cattle behavioural patterns recorded using collar systems fitted to individual dairy cows to infer their feeding behaviors. Cattle tag sensory system, consist of a piezoelectric micro-electromechanical chip containing a 3-axis accelerometer and a 3-axis magneto-resistive sensor (HMC6343 -Honeywell, Plymouth, MN), data were collected at the Tasmanian Institute of Agriculture (TIA) Dairy Research Facility in Tasmania. A multi-classifier pattern recognition system was applied to classify five common cattle behaviour classes, namely, Grazing, Ruminating, Resting, Walking, and Scratching. Part of the recorded cattle tag data were labeled with the known behavioural patterns observed by the field experimental scientists. Pattern recognition system had a sensory data preprocessor to extract window based statistical features from the time series data, and a supervised multi-classifier system to learn the extracted features and generate a model to classify unknown data into one of the five behaviour classes.

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An investigation of cow feeding behavior using motion sensors

Cited by 17 publications

References 12 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

Cattle behaviour classification using 3-axis collar sensor and multi-classifier pattern recognition

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