# AI-Enabled Animal Behavior Analysis with High Usability: A Case Study on Open-Field Experiments
## 1 Introduction  
Animal behavior is the body language by which an animal expresses its psychological and physiological state and its overall function. Typical model animals, such as mice, rabbits, and goats, are widely used to analyze different behaviors in the open field to measure the effectiveness of experiments in biology, toxicology, neuroscience, pharmacology, animal husbandry, and genetics [1; 2; 3]. Due to the advancement of embedded technology and automation technology, animal behavior recognition has progressed rapidly. For example, [PERSON] et al. [4] utilize embedded devices and corresponding behavior recognition methods for efficient behavior recognition of livestock, such as cows. [PERSON] et al. [5] proposed automation technology to predict the behavioral changes of mice undergoing surgery and observe the effects of painkillers. With the development of deep learning techniques, the performance of animal behavior recognition has been significantly improved. [PERSON] et al. [6] achieve high-accuracy detection of wild-animal behavior using deep learning models. In order to ensure real-time performance, [PERSON] et al. [7] proposed a behavior recognition algorithm for cattle based on a spatial-and-temporal information framework. Despite the success of these studies in specific scenarios, these studies generally need to rely on commercialized tools to provide convenient human-computer interaction and data retrieval. Although commercialized tools can simplify human-computer interaction and reduce labor costs for behavior recognition, they still need more flexibility, scalability, and interactivity. Specifically, these tools usually can only detect specific behaviors, making it difficult to adapt to different experimental needs. In addition, designing the underlying algorithms of commercialized tools is often scenario-specific. It cannot generate intermediate results such as single-frame pose data, limiting its application scope. The limitations of hardware and architecture also make it difficult for these tools to change or expand the underlying algorithms. It affects the accuracy and speed of the experimental results. Finally, commercialized tools have a limited scope of human-computer interaction for data retrieval and analysis, and researchers need to resort to other specialized tools or programming languages, such as structured query language (SQL) or Python, which increases the learning cost and reduces the usability of the tools. Therefore, designing and implementing a laboratory animal behavior analysis platform that can efficiently identify animal behavioral actions, effectively manage animal behavioral data, support changes in the underlying algorithms, and provide convenient human-computer interaction capabilities is of great significance in reducing the workload of related researchers. In order to compensate for the shortcomings of existing tools and to design a highly usable platform for animal behavior analysis, we established three goals that the platform should achieve:  
1. The platform should be flexible enough to support researchers in selecting different behavior recognition methods and behavior detection categories. 2. The platform should be scalable to support researchers in upgrading or expanding the underlying algorithms. 3. The platform should have flexible and convenient interactivity so that researchers can use the platform's preset human-computer interaction functions when using experimental data systems or commercial tools for data querying and statistical analysis or use more adaptive interaction methods to meet the changing functional needs of researchers. When designing the platform, we integrated various architecture design methods, such as microservices and plug-in design, to support researchers in flexibly configuring the detection methods and behavioral categories and having good flexibility and scalability.