# Research Progress in the Early Warning of Chicken Diseases by Monitoring Clinical Symptoms
## 3 Early Disease Detection through Behavioral Characteristics
### Abnormal Posture  
Activity is the dynamic characteristics of the chicken, while posture is concerned with the static characteristics. Therefore, it is not difficult to understand that when the body function deviates from homeostasis, the chicken does not often have enough energy to maintain a normal posture.  
[PERSON] et al. [88] have successfully identified healthy and AI-infected chickens by observing posture changes in broilers. In their study, segmentation algorithms were used to separate the background and chickens first to extract the contour and skeleton information. Then, six eigenvalues (concavity, skeleton attitude angle, shape features, area-linear rate, elongation and circularity) were calculated manually to constitute the eigenvectors, and finally, machine learning algorithms were built to evaluate the health status of broilers, and the results showed that the SVM model had the highest accuracy of 99.469%. The algorithm can automatically detect sick chickens to a certain extent to achieve the purpose of early warning, but the procedures are complicated. Before the health status assessment, the head area of chickens needs to be extracted to determine whether they are in the feeding state. This means the algorithm will not work when the chickens are not completely facing the camera. To analyze the postural differences between healthy and sick chickens infected with Newcastle disease virus, [PERSON] et al. [89] designed and calculated another set of posture shape features (circle variance, elongation, convexity, complexity and eccentricity), in which walk speed was also included in the eigenvector in their study. The results suggested that early warning can be achieved before a major outbreak occurs. The earliest possible infection detection time was on the fourth day based on circle variance and elongation and the sixth day based on eccentricity and walk speed. Similarly, the SVM model achieved the highest accuracy of 98.8%. Still, the difference is that this method only needs to obtain the top view of the chicken to carry out subsequent recognition, which reduces the dependence on the orientation of the living animal when the image is obtained.  
The algorithm based on manual feature extraction can only extract the features that are easy to quantify, such as size, shape, texture, etc., and has a large workload and finds it difficult to achieve model migration for different tasks. Therefore, the poultry posture recognition methods based on deep learning came into being. To automatically detect sick broilers in a flock, [PERSON] et al. [90] proposed an optimized model (Improved Feature Fusion Single Shot MultiBox Detector) based on the original Single Shot MultiBox Detector model, which can detect and identify the health status of broilers simultaneously, and achieved 99.7% and 48.1% mean average precision (intersection over union > 0.5, 0.9, respectively). In addition, if the automatic and accurate classification of different animal postures such as standing, sleeping, food pecking and feather pecking can be achieved, this will provide farmers with better information and further promote efficient flock management [91; 92; 93].