# AI-Enabled Animal Behavior Analysis with High Usability: A Case Study on Open-Field Experiments
## 2 Related Work  
In recent years, the field of animal behavior analysis has made remarkable progress because of the application of commercial tools and advanced behavior recognition algorithms. These techniques not only improve research efficiency but also provide strong support for animal welfare and disease research. **Commercial tools. ** EthoWatcher is an open-source software designed to record and analyze animal behavior [8]. It can process video files and offers rich features to label and quantify animal movement information. EthoWatcher provides a user-friendly interface for various experimental setups, which makes it easy for researchers to perform behavioral analyses. The ToxTrac software utilizes a second-order Kalman filter to estimate a detected object's trajectory and can fuse existing trajectory segments to generate a complete trajectory [9]. ToxTrac also provides various tools and features for analyzing animal behavior, such as path length, average velocity, and dwell time. These features make ToxTrac a powerful tool in animal behavior research. ANY-maze is an animal behavior analysis system developed by Stoelting, Inc., Kiel, WI, USA [10]. By marking a point on the back of a mouse, ANY-maze can calculate the distance the mouse moves in the open field, thus determining the mouse's locomotor ability. Although various parameters can be generated  
Figure 1: Overall structure of the platform. automatically, statistical analysis software such as GraphPad is required to analyze the differences between disease and normal mice [11]. **Animal behavior recognition algorithm. ** With the development of machine learning and deep learning technology, many scholars have started to apply methods based on machine learning and deep learning to animal behavior recognition, and they have achieved good results. [PERSON] et al. [12] proposed an animal behavior classification method based on six features (keypoint location, depression, skeleton, shape feature, skeleton angle, and elongation) and a naive Bayes model (NBM), which can effectively identify and classify the daily behaviors of animals. In order to improve detection accuracy, [PERSON] et al. [13] fully utilized the advantage of long short-term memory (LSTM) in processing time series data. They accurately assessed the lameness status of broilers by successively extracting keypoints into the LSTM model and classifying the lameness degree of broilers according to the six-point assessment method. To further improve detection accuracy [PERSON] et al. [14] first estimated bird keypoints using HRNet to generate global and local features [15]. After that, the excitation region was localized by keypoint clustering. Finally, bird behavior recognition achieved significant results by combining ResNet [16]. In order to compensate for the lack of single morphological features, [PERSON] et al. [17] fused multi-features to realize efficient lameness classification, including red-green-blue (RGB), optical flow, and skeleton. They utilized VGG-19 to extract skeleton joint point features and analyze spatiotemporal features by ST-GCN [18; 19]. [PERSON] et al. [20] ameliorated the deep learning method for pig aggression behavior recognition, using video data as input and extracting temporal and spatial features based on VGG-16 and LSTM models [18]. Its recognition accuracy reached 98.4% and significantly improves prediction efficiency. In order to meet the demand for real-time monitoring in the production environment, [PERSON] et al. [21] designed a real-time sow behavior detection algorithm (SBDA-DL), based on MobileNet and a single-shot multi-box detector (SSD). They trained and predicted sow behaviors, including watering, unirating, and crawling, and obtained satisfactory results. Moreover, several methods focus on behavioral recognition detection in complex wild environments. For example, [PERSON] et al. [22] used an infrared camera to capture the activity of deer, wild boar, fox, and hare in the wild environment.