## 2 Software Description
### Software Interface  
A screenshot of AniWellTracker interface is shown in Figure 2. The software provides toolbar buttons that can be selected by clicking on the tracking or review mode, as well as buttons to turn on the built-in chart visualization window. The canvas window shows the source image or the analyzed result, and the ROI area is marked with a red circle or rectangle. Users can enlarge or reduce the image by pressing the magnifying glass icons, and by pressing the Animate button, the source image and the image processing result can be displayed alternately. The file manager window shows a list of the image files in the folder to be analyzed. When users select an image file, the image is immediately displayed on the canvas window. In the tracking mode, the tracker window that can analyze images continuously is shown (Figure 2). Image analysis is performed sequentially in three steps: \"(1) Binarization\", \"(2) Region extraction\", and \"(3) Processing\". In \"(1) Binarization\", a color image is converted into a black and white image (binary image), and in \"(2) Region extraction\", white blobs (i.e., potentially animals) are detected from the binary image, and centroid coordinates and animal area is calculated. Users can easily test multiple parameters to obtain the best results (Figure 3). In \"(3) Processing\", analysis is performed on all image files in the source folder. Figure 1: Software components and processing flows. Red and blue arrows indicate data flow and user action flow, respectively. When image analysis is complete, the user can review and visualize the results in the \"Review\" window (Figure 4). First, the first \"Image\" tab has a function to automatically save as a file by displaying the tracking path in the original image such that the tracking result can be reviewed. Not only for one well but also the entire image, the path of the specified length (number of files) is displayed on one original image, allowing for a quick review to check if there are any tracking issues. These functions are useful for the quality of the tracking path. Figure 3: Image processing to detect centroids of individual animals. (**A**) Adaptive threshold window for binarization. (**B**) Region labeling window for animal detection and calculation of image analysis parameters. Figure 2: Software interface. control of data. In the second \"Reprocessing\" tab, the user reprocesses the original files, and if the user sets the duration (min), the average speed and distance are recalculated for each time interval and saved in the output file. From the centroid coordinates recorded in the output file, the speed and moving distance at the time that each image was captured were recorded. Because the amount of raw data of speed and distance is almost equal to the number of images, it is useful to easily observe the overall trend of change in speed and distance traveled by calculating the average speed and distance traveled for a specific period (e.g., 10 min). The third \"Time Series Heatmap\" tab features a heatmap display of the raw data for speed, distance traveled, and average speed versus time (Figure 4A). When a user presses buttons, such as \"Travel speed (every data)\" or \"Travel distance (every data)\", the analysis result can be immediately displayed in a heatmap chart. The time interval to be displayed on the heatmap can be input in minutes or hours, and the color corresponding to the numerical value can be set by inputting a range of numerical values (Figure 4B). In addition, in the \"Location heatmap\" tab, the probability of the location where the animal stays can be displayed as a heatmap (Figure 4C,D), and in the \"Distance from center heatmap\" tab, the location of the radius where the animal remains from the center of the well can be displayed as a chart. Figure 4: Post-data processing and visualization in the Reviewer window. **(A)** In the Time series heatmap tab, various analysis endpoints can be drawn as a time series heatmap chart.