Classification of broiler behaviours using triaxial accelerometer and machine learning.

Understanding broiler behaviours provides important implications for animal well-being and farm management. The objectives of this study were to classify specific broiler behaviours by analysing data from wearable accelerometers using two machine learning models, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM). Lightweight triaxial accelerometers were used to record accelerations of nine 7-week-old broilers at a sampling frequency of 40 Hz. A total of 261.6-min data were labelled for four behaviours - walking, resting, feeding and drinking. Instantaneous motion features including magnitude area, vector magnitude, movement variation, energy, and entropy were extracted and stored in a dataset which was then segmented by one of the six window lengths (1, 3, 5, 7, 10 and 20 s) with 50% overlap between consecutive windows. The mean, variation, SD, minimum and maximum of each instantaneous motion feature and two-way correlations of acceleration data were calculated within each window, yielding a total of 43 statistic features for training and testing of machine learning models. Performance of the models was evaluated using pure behaviour datasets (single behaviour type per dataset) and continuous behaviour datasets (continuous recording that involved multiple behaviour types per dataset). For pure behaviour datasets, both KNN and SVM models showed high sensitivities in classifying broiler resting (87% and 85%, respectively) and walking (99% and 99%, respectively). The accuracies of SVM were higher than KNN in differentiating feeding (88% and 75%, respectively) and drinking (83% and 62%, respectively) behaviours. Sliding window with 1-s length yielded the best performance for classifying continuous behaviour datasets. The performance of classification model generally improved as more birds were included for training. In conclusion, classification of specific broiler behaviours can be achieved by recording bird triaxial accelerations and analysing acceleration data through machine learning. Performances of different machine learning models differ in classifying specific broiler behaviours.

Live en face imaging of aortic valve leaflets under mechanical stress.

Soft tissues, such as tendons, skin, arteries, or lung, are constantly subject to mechanical stresses in vivo. None more so than the aortic heart valve that experiences an array of forces including shear stress, cyclic pressure, strain, and flexion. Anisotropic biaxial cyclic stretch maintains valve homeostasis; however, abnormal forces are implicated in disease progression. The response of the valve endothelium to deviations from physiological levels has not been fully characterized. Here, we show the design and validation of a novel stretch apparatus capable of applying biaxial stretch to viable heart valve tissue, while simultaneously allowing for live en face endothelial cell imaging via confocal laser scanning microscopy (CLSM). Real-time imaging of tissue is possible while undergoing highly characterized mechanical conditions and maintaining the native extracellular matrix. Thus, it provides significant advantages over traditional cell culture or in vivo animal models. Planar biaxial tissue stretching with simultaneous live cell imaging could prove useful in studying the mechanobiology of any soft tissue.