In experimental research, animal welfare should always be of the highest priority. Currently, physical in-person observations are the standard. This is time consuming, and results are subjective. Video-based machine learning models to monitor experimental pigs provides a continuous and objective observation method for animal misthrive detection. The aim of this study was to develop and validate a pig tracking technology, using video-based data in a machine learning model to analyze posture and activity level of experimental pigs living in single-pig pens. A research prototype was created using a microcomputer and a ceiling mounted camera for live recording based on the obtained images from the experimental facility and a combined model was created based on the Ultralytics YOLOv8n for object detection trained on the obtained images. As a second step, the Lucas-Kanade sparse optical flow technique for movement detection was applied. The resulting model successfully classified whether individual pigs were laying, standing, or walking. The validation test showed an accuracy of 90.66%, precision of 90.91%, recall of 90.66%, and a correlation coefficient of 84.53% compared with observed ground truth. In conclusion, the model demonstrates how machine learning can be used to monitor experimental animals potentially to improve animal welfare.