Lars Schmidt Hansen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Lars Schmidt Hansen has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Anna Krarup Keller
Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
2 shared publications - Constanca Figueiredo
Institute of Transfusion Medicine and Transplant Engineering, Hannover Medical School, 30625 Hannover, Germany
2 shared publications - Cyril Moers
Department of Surgery, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
2 shared publications - Frederik Larsen
Department of Electro- and Computer technology, Section for Biomedical Engineering, Aarhus University, 8000 Aarhus, Denmark
2 shared publications - Marc Gjern Weiss
Department of Clinical Medicine, Aarhus University, 8000 Aarhus, Denmark
2 shared publications - Stefan Wagner
Aarhus University
2 shared publications
Latest Publications
- Open Access
Machine-Learning-Based Activity Tracking for Individual Pig Monitoring in Experimental Facilities for Improved Animal Welfare in Research
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 for monitoring experimental pigs provide a continuous and objective observation method for animal misthriving 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 the 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 lying, standing, or walking. The validation test showed an accuracy of 90.66%, precision of 90.91%, recall of 90.66%, and 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 to potentially improve animal welfare.
Open AccessMachine-Learning-Based Activity Tracking for Individual Pig Monitoring in Experimental Facilities for Improved Animal Welfare in Research
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 for monitoring experimental pigs provide a continuous and objective observation method for animal misthriving 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 the 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 lying, standing, or walking. The validation test showed an accuracy of 90.66%, precision of 90.91%, recall of 90.66%, and 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 to potentially improve animal welfare.
- Open Access
- 20 December 2024
- Published by MDPI AG in Preprints.org
Machine Learning-Based Activity Tracking for Individual Pig Monitoring in Experimental Facilities for Improved Animal Welfare in Research
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.
- 20 December 2024
- Published by MDPI AG in Preprints.org
Open AccessMachine Learning-Based Activity Tracking for Individual Pig Monitoring in Experimental Facilities for Improved Animal Welfare in Research
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.