# Research Progress in the Early Warning of Chicken Diseases by Monitoring Clinical Symptoms
## 3 Early Disease Detection through Behavioral Characteristics
### Abnormal Activity  
Lampleness is a common leg disease in poultry and approximately 30% of the chickens were seriously lame [76]. When poultry activity deviates from normal thresholds, it often indicates the presence of disease. Recent studies have shown that automatic monitoring of flock activity can provide early warning of disease.  
[PERSON] et al. [77] used cameras to record and describe the undisturbed and abnormal activity levels of broiler chickens at 1, 2 and 3 weeks old, and the results showed that the detection system could notify producers when the activity levels deviated from an expected level at a given age. A gait score is the most commonly used method to detect lameness, which heavily relies on visual observation. To determine automatic monitoring of the activity level of broilers, a new and non-invasive method was developed. [PERSON] [78] utilized a 3D vision camera with a depth sensor to record the behaviors of broilers, and an image processing algorithm was employed to detect the lying and standing events of broilers and count the number and duration time of lying events. The experiment proved that the classification accuracy of 3D visual camera system was 93% compared with manual marking results, and a method for indirectly evaluating the health status of broilers' legs was proposed according to the negative correlation between lying duration time and gait score. Later on, [PERSON] [79] found that speed, step frequency, step length and lateral body oscillation of the broilers were also correlated with gait scores. Furthermore, the amplitude (difference between highest activity peak and baseline level) was proved to be significantly related to gait score, which will decrease with the declining walking ability [80].  
Optical flow analysis, a simple image analysis technique that can calculate the moving velocity of an object on the image plane based on the change in brightness between consecutive images, was used to detect abnormal flock activity at an early stage [81]. [PERSON] et al. [82] used optical flow analysis to detect chickens infected with Campylobacter, and the results showed that Campylobacter-free chicken flocks have higher mean and lower kurtosis of optical flow than Campylobacter-positive ones. The subclinical infection can be identified within the first 7-10 days in this way, which is much earlier than traditional microbiological methods. Flock activity monitoring has also been useful for the early detection of abnormal conditions associated with increased mortality and keel bone fractures in laying hens [83; 84]. Furthermore, the flock activity of chickens can be translated into an animal distribution index, which has the potential to indicate some underlying animal health problems [85; 86; 87].  
The above results suggest that automatic monitoring of poultry activity can detect disease at an early stage. However, most of the current related studies focus on flocks, and how to find the single suspected diseased chicken from the group with abnormal activity will become another breakthrough in improving animal welfare.