# AI-Enabled Animal Behavior Analysis with High Usability: A Case Study on Open-Field Experiments
## 3 Overall System Architecture
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multirow{2}{*}{**Research Object**} & \multirow{2}{*}{**Behavior Type**} & \multicolumn{2}{c}{**Behavior Recognition**} & \multirow{2}{*}{**Data Retrieval and Analysis**} & \multirow{2}{*}{**Scalability**} & \multirow{2}{*}{**Interactivity**} \\  & & & & **Method** & & & \\ \hline \multirow{3}{*}{Fang} & \multirow{3}{*}{Broiler chicken} & \multicolumn{2}{c}{Standing,} & \multirow{3}{*}{Naive Bayes} & \multirow{3}{*}{Data retrieval and analysis rely on other tools} & \multirow{3}{*}{-} & \multirow{3}{*}{Command line} \\  & & & & & & \\ \hline \multirow{3}{*}{Nasiri} & \multirow{3}{*}{Broiler chicken} & \multirow{3}{*}{Limp} & \multirow{3}{*}{LSTM} & \multirow{3}{*}{Data retrieval and analysis rely on other tools} & \multirow{3}{*}{-} & \multirow{3}{*}{Command line} \\  & & & & & & \\ \hline \multirow{3}{*}{Lin} & \multirow{3}{*}{Bird} & \multicolumn{2}{c}{Swimming,} & \multirow{3}{*}{ResNet18} & \multirow{3}{*}{-} & \multirow{3}{*}{Command line} \\  & & & & & & \\ \cline{1-1}  & & & & & & \\ \cline{1-1}  & & & & & & \\ \hline \multirow{3}{*}{Li} & \multirow{3}{*}{Dairy cattle} & \multirow{3}{*}{Limp} & \multirow{3}{*}{ST-GCN} & \multirow{3}{*}{Data retrieval and analysis rely on other tools} & \multirow{3}{*}{-} & \multirow{3}{*}{Command line} \\ \cline{1-1}  & & & & & & \\ \hline \multirow{3}{*}{Ours} & \multirow{3}{*}{Animals} & \multirow{3}{*}{Customizable} & \multirow{3}{*}{Replaceable} & \multirow{3}{*}{Contains certain functions and natural language query interface} & Supporting algorithm replacement & Graphical interface and natural language query interface \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._The platform has two working modes--training and inference--to adapt to different practical use cases. In the training mode, researchers first need to input different training information, including animal category, number of key points, number of behavioral categories, etc., through the front-end page according to the different algorithms selected, then import and label the training data. Specifically, the labeled key point data will be used to train within the pose estimation model, while the labeled behavioral category data will be used to train the behavioral recognition model. Once the training starts, the training information and progress will be fed back to the researcher in real time through the front-end page. In the inference mode, the video capture and sending module will first send the captured video data to the receiving and preprocessing module on the server side. Then, after the preprocessing module processes the video data, there will be two different kinds of subsequent processing depending on the requirements. One is to input the preprocessed data into the pose estimation module, and the obtained pose estimation result will be temporarily stored as an intermediate result at the service end. Then, the posture estimation results are input into the behavior recognition model to generate specific behavioral category information, which is then stored in the database. The other is to directly input the preprocessed data into the behavior recognition model for analysis and keep the analysis results. Once reasoning is complete, researchers can describe their data retrieval or analysis needs in natural language on the front-end page. These requirements are sent, received, processed, and then fed into the natural speech query interface module, which converts the requirements into commands understandable by the platform and executes them. Finally, the query results are displayed to the researchers through the front-end page.