# AI-Enabled Animal Behavior Analysis with High Usability: A Case Study on Open-Field Experiments
## 7 Conclusions  
We propose an AI-enabled and highly available animal behavior analysis platform, which has been applied to a medical experimental institution in China to evaluate the behavioral differences between disease model mice and normal mice in a specific case. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Total Sample Size** & **Correct Amount** & **Execution Accuracy** \\ \hline Easy & 272 & 267 & 0.98 \\ Medium & 146 & 137 & 0.93 \\ Hard & 93 & 81 & 0.87 \\ Aggregation & 511 & 485 & 0.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The accuracy performance of RAT-SQL. Figure 10: Seven Common Relationship Types in ConceptNet. In this case, the platform obtained the experimental video through the video capture device. Then, it used the pose estimation model to extract the single-frame pose features of experimental animals. Then, the platform used the behavior recognition model to process the continuous single-frame pose features to capture and store experimental animals' behavior information automatically. In the platform's design, we mainly focused on how to improve the flexibility and scalability of behavior recognition and the interactivity of the platform to reduce the learning cost for researchers to use the platform and improve its usability. Therefore, the platform architecture integrates plug-in management, unified interface, and algorithm library design ideas so that it can be flexibly configured and extended. Whether high-precision behavior recognition can be achieved in animal behavior experiments is often one of many evaluation standards. The intermediate result output in the recognition process, such as the pose data of each frame, is also of great significance. Flexible algorithm replacement and expansion can enable researchers to choose more suitable identification methods according to actual needs and make the integration of algorithm upgrading and other algorithms more convenient, making the platform more competitive in the flexibility and scalability of algorithms. The platform mainly relies on three core services. The pose estimation service performs pose estimation on experimental animals in the same experimental environment to obtain each animal's key point coordinate information. The behavior recognition service extracts and classifies the behavior feature vector of each animal in the video frame in different ways according to the selected algorithm. The natural language query interface service can convert the researchers' natural language query requirements into executable SQL statements and obtain the corresponding results from the database. The natural language query interface service provides more flexible and efficient information retrieval and improves the platform's interaction. Based on these three core services, we have built a highly available animal behavior analysis platform, which not only realizes the automatic identification of animal behavior but also enables researchers to flexibly select behavior recognition algorithms through the algorithm library, eliminating technical barriers and reducing researchers' dependence on experts. By utilizing natural language query interface services, the platform can open data access to all researchers and provide higher usability behavioral interactions. The platform relies on computer vision technology in deep learning. However, the recognition effect may be affected when the video quality is too low, or the limbs between animals are blocked too much. In addition, in the natural language query interface service, researchers' inaccurate language description may also affect the effect of information retrieval. In order to further improve the availability of the platform, we plan to reduce the dependence of the platform on video quality and improve the accuracy of behavior recognition.