Characterization of the on-body path Loss at 2.45 GHz and energy efficient WBAN design for dairy cows

Wireless body area networks (WBANs) provide promising applications in the healthcare monitoring of dairy cows. The characterization of the path loss (PL) between on-body nodes constitutes an important step in the deployment of a WBAN. In this paper, the PL between nodes placed on the body of a dairy cow was determined at 2.45 GHz. Finite-difference time domain simulations with two half-wavelength dipoles placed 20 mm above a cow model were performed using a 3-D electromagnetic solver. Measurements were conducted on a live cow to validate the simulation results. Excellent agreement between measurements and simulations was achieved and the obtained PL values as a function of the transmitter-receiver separation were well fitted by a lognormal PL model with a PL exponent of 3.1 and a PL at reference distance ( 10 cm) of 44 dB. As an application, the packet error rate ( PER) and the energy efficiency of different WBAN topologies for dairy cows (i.e., single-hop, multihop, and cooperative networks) were investigated. The analysis results revealed that exploiting multihop and cooperative communication schemes decrease the PER and increase the optimal payload packet size. The analysis results revealed that exploiting multihop and cooperative communication schemes increase the optimal payload packet size and improve the energy efficiency by 30%

On the use of on-cow accelerometers for the classification of behaviours in dairy barns

Analysing behaviours can provide insight into the health and overall well-being of dairy cows. Automatic monitoring systems using e.g., accelerometers are becoming increasingly important to accurately quantify cows' behaviours as the herd size increases. The aim of this study is to automatically classify cows' behaviours by comparing leg- and neck-mounted accelerometers, and to study the effect of the sampling rate and the number of accelerometer axes logged on the classification performances. Lying, standing, and feeding behaviours of 16 different lactating dairy cows were logged for 6 h with 3D-accelerometers. The behaviours were simultaneously recorded using visual observation and video recordings as a reference. Different features were extracted from the raw data and machine learning algorithms were used for the classification. The classification models using combined data of the neck- and the leg-mounted accelerometers have classified the three behaviours with high precision (80-99%) and sensitivity (87-99%). For the leg-mounted accelerometer, lying behaviour was classified with high precision (99%) and sensitivity (98%). Feeding was classified more accurately by the neck-mounted versus the leg-mounted accelerometer (precision 92% versus 80%; sensitivity 97% versus 88%). Standing was the most difficult behaviour to classify when only one accelerometer was used. In addition, the classification performances were not highly influenced when only X, X and Z, or Z and Y axes were used for the classification instead of three axes, especially for the neck-mounted accelerometer. Moreover, the accuracy of the models decreased with about 20% when the sampling rate was decreased from 1 Hz to 0.05 Hz

Localization and accelerometer sensors for the detection of oestrus in dairy cattle

The aim of this work was to combine ultra-wide band (UWB) localisation tracking, a neck-mounted accelerometer and a leg-mounted accelerometer for the detection of oestrus in dairy cows. Twelve Holstein cows with successful artificial insemination (AI) were used in this study. The sensors were attached two weeks before the expected day of oestrus and removed after AI. Different cow variables (e.g. lying time, number of steps, ruminating time, travelled distance) were extracted from the raw sensor data and used to build and test the detection models. Logistic regression models were developed for each individual sensor as well as for each combination of sensors (two or three). The performances were similar when one sensor was used only as when combining the neck- and leg-mounted accelerometer (sensitivity (Se) =75-78%, area under curve (AUC) =93-94%). The performance increased when localisation was combined with either the neck- or leg-mounted accelerometer, especially for the sensitivity (80% for leg accelerometer + localisation and 88% for neck accelerometer + localisation). The AUC were nearly the same (97%). The best performance was obtained with the combination of all three sensors (Se = 90%, AUC = 99%). Future work will consist of expanding this research to other herds with larger sample size as well as considering cows’ anomalies (e.g. mastitis, lameness) and other sensors (e.g. bolus or eartag to measure the temperature)

Classification of ingestive-related cow behaviours using RumiWatch halter and neck-mounted accelerometers

A new simple decision-tree (DT) algorithm was developed using the data from a neck-mounted accelerometer for real-time classification of feeding and ruminating behaviours of dairy cows. The performance of the DT was compared to that of a support vector machine (SVM) algorithm and a RumiWatch noseband sensor and the effect of decreasing the sampling rate of the accelerometer on the classification accuracy of the developed algorithms was investigated. Ten multiparous dairy cows were used in this study. Each cow was fitted with a RumiWatch halter and an accelerometer attached to the cow's collar with both sensors programmed to log data at 10 Hz. Direct observations of the cows' behaviours were used as reference (baseline data). Results indicate that the two sensors have similar classification performances for the considered behavioural categories (i.e., feeding, ruminating, other activity), with an overall accuracy of 93% for the accelerometer with SVM, 90% for the accelerometer with DT, and 91% for the Rumiwatch sensor. The difference between the predicted and the observed ruminating time (in min/h) was less than 1 min. h (1.5% of the observed time) for the SVM and less than 2 min. h (2.8%) for both DT and the RumiWatch. Similarly, the difference in feeding time was 1.3 min. h (2.1%) for the SVM compared to 2.5 min. h (4.3%) and 2.4 min. h (4.1%) for both RumiWatch and DT, respectively. These preliminary findings illustrate the potential of the collar-mounted accelerometer to classify feeding and ruminating behaviours with accuracy measures comparable to the Rumiwatch noseband sensor

Calving and estrus detection in dairy cattle using a combination of indoor localization and accelerometer sensors

Accelerometers (neck- and leg-mounted) and ultra-wide band (UWB) indoor localization sensors were combined for the detection of calving and estrus in dairy cattle. In total, 13 pregnant cows and 12 cows with successful insemination were used in this study. Data were collected two weeks before and two weeks after delivery for calving. Similarly, data were collected two weeks before and two weeks after artificial insemination (AI) for estrus. Different cow variables were extracted from the raw data (e.g., lying time, number of steps, ruminating time, travelled distance) and used to build and test the detection models. Logistic regression models were developed for each individual sensor as well as for each combination of sensors (two or three) for both calving and estrus. Moreover, the detection performance within different time intervals (24 h, 12 h, 8 h, 4 h, and 2 h) before calving and AI was investigated. In general, for both calving and estrus, the performance of the detection within 2-4 h was lower than for 8 h24 h. However, the use of a combination of sensors increased the performance for all investigated detection time intervals. For calving, similar results were obtained for the detection within 24 h, 12 h, and 8 h. When one sensor was used for calving detection within 24-8 h, the localization sensor performed best (Precision (Pr) 73-77%, Sensitivity (Se) 57-58%, Area under curve (AUC) 90-91%), followed by the leg-mounted accelerometer (Pr 67-77%, Se 54-55%, AUC = 88-90%) and the neck-mounted accelerometer (Pr 50-53%, Se 47-48%, AUC = 86-88%). As for calving, the results of estrus were similar for the time intervals 24 h-8 h. In this case, similar results were obtained when using any of the three sensors separately as when combining a neck- and a leg-mounted accelerometers (Pr 86-89%, Se 73-77%). For both calving and estrus, the performance improved when localization was combined with either the neck- or leg-mounted accelerometer, especially for the sensitivity (73-91%). Finally, for the detection with one sensor within a time interval of 4 h or 2 h, the Pr and Se decreased to 55-65% and 42-62% for estrus and to 40-63% and 33-40% for calving. However, the combination of localization with either leg or neck-mounted accelerometer as well as the combination of the three sensors improved the Pr and Se compared to one sensor (Pr 72-87%, Se 63-85%). This study demonstrates the potential of combining different sensors in order to develop a multi-functional monitoring system for dairy cattle

Internet of animals: on-and off-body propagation analysis for energy efficient WBAN design for dairy cows

This paper presents propagation modelling of different on-body and off-body wireless communication scenarios for dairy cows in barns at 2.4 GHz. Based on the obtained propagation models, a WBAN that monitors multiple heath parameters is designed for optimal performances in terms of energy efficiency and packet error rate