Arndt, S.S., Goerlich, V.C., van der Staay, F.J. (2022). A dynamic concept of animal welfare: The role of appetitive and adverse internal and external factors and the animal’s ability to adapt to them. Frontiers in Animal Science 3. https://doi.org/10.3389/fanim.2022.908513.
Lorbach, M., Poppe, R., Veltkamp, R.C. (2019). Interactive rodent behavior annotation in video using active learning. Multimedia Tools and Applications, 78:19787–19806. https://doi.org/10.1007/s11042-019-7169-4.
Locomotion and activity
Parmentier, J.I.M., Bosch, S., Weishaupt, M.A., Gmel, A.I., Havinga, P.J.M., van Weeren, P.R., Serra Braganca, F.M. (2023). Prediction of continuous and discrete kinetic parameters in horses from inertial measurement units data using recurrent artificial neural networks. Scientific Reports, 13[740]. https://doi.org/10.1038/s41598-023-27899-4.
Darbandi, H., Serra Braganca, F., van der Zwaag, B.J., Havinga, P. (2022). Accurate Horse Gait Event Estimation Using an Inertial Sensor Mounted on Different Body Locations. IEEE. https://doi.org/10.1109/SMARTCOMP55677.2022.00076.
Serra Bragança, F.M., Broomé, S., Rhodin, M., Björnsdóttir, S., Gunnarsson, V., Voskamp, J.P., Persson-Sjodin, E., Back, W., Lindgren, G., Novoa-Bravo, M., Roepstorff, C., van der Zwaag, B.J., van Weeren, P.R., Hernlund, E. (2020). Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning. Scientific Reports, 10(1), [17785]. https://doi.org/10.1038/s41598-020-73215-9.
Automated welfare monitoring in different settings
Pessanha, F., Salah, A.A., van Loon, T., Veltkamp, R. (2022). Facial image-based automatic assessment of equine pain. IEEE Trans. Affective Computing. https://doi.org/10.1109/TAFFC.2022.3177639.
van der Sluis, M., Ellen, E.D., de Klerk, B., Rodenburg, T.B., de Haas, Y. (2021). The relationship between gait and automated recordings of individual broiler activity levels. Poultry Science, 100(9):101300. https://doi.org/10.1016/j.psj.2021.101300.
van der Zande, L. E., Guzhva, O., Rodenburg, T.B. (2021). Individual detection and tracking of group housed pigs in their home pen using computer vision. Frontiers in Animal Science 2(10). https://doi.org/10.3389/fanim.2021.669312.
van der Laan, J.E., Vinke, C.M., van der Borg, J.A.M., Arndt, S.S. (2021). Restless nights? Nocturnal activity as a useful indicator of adaptability of shelter housed dogs. Applied Animal Behaviour Science 241:105377. https://doi.org/10.1016/j.applanim.2021.105377.
Human-animal interaction
van Houtert, E.A.E., Rodenburg, T.B., Vermetten, E., Endenburg, N. (2022). The impact of service dogs on military veterans and (ex) first aid responders with Post-traumatic Stress Disorder. Frontiers in Psychiatry 13. https://doi.org/10.3389/fpsyt.2022.834291.
Kapteijn, C.M., Frippiat, T., van Beckhoven, C., van Lith, H.A., Endenburg, N., Vermetten, E., Rodenburg, T.B. (2022). Measuring heart rate variability using a heart rate monitor in horses (Equus caballus) during groundwork. Frontiers in Veterinary Science 9. https://doi.org/10.3389/fvets.2022.939534.
van Gemeren, C., Poppe, R., Veltkamp, R.C. (2018). Hands-on: deformable pose and motion models for spatiotemporal localization of fine-grained dyadic interactions. EURASIP Journal on Image and Video Processing, 16. https://doi.org/10.1186/s13640-018-0255-0.
Integration of data and techniques
Liseune, A., Salamone, M., van den Poel, D., van Ranst, B., Hostens, M. (2021). Predicting the milk yield curve of dairy cows in the subsequent lactation period using deep learning. Computers and Electronics in Agriculture, 180:105904. https://doi.org/10.1016/j.compag.2020.105904.
Liseune, A., van den Poel, D., Hut, P.R., van Eerdenburg, F.J., Hostens, M. (2021). Leveraging sequential information from multivariate behavioral sensor data to predict the moment of calving in dairy cattle using deep learning. Computers and Electronics in Agriculture, 191:106566. https://doi.org/10.1016/j.compag.2021.106566.
Liseune, A., Salamone, M., van den Poel, D., van Ranst, B., Hostens, M. (2020). Leveraging latent representations for milk yield prediction and interpolation using deep learning. Computers and Electronics in Agriculture, 175:105600. https://doi.org/10.1016/j.compag.2020.10560.
Responsible use of AI
Giersberg, M.F., Meijboom, F.L.B. (2022). Caught on camera: on the need of responsible use of video observation for animal behavior and welfare research. Frontiers in Veterinary Science 9. https://doi.org/10.3389/fvets.2022.864677.
van Putten, A., Giersberg, M.F., Meijboom, F.L.B. (2022). 75. Do we improve any aspects of animal welfare by implementing Computer Vision in livestock farming? In: Bruce, D., Bruce, A. (Eds).: Transforming food systems: Ethics, innovation and responsibility, EurSafe 2022, Edinburgh, United Kingdom, 07.09.-10.09.2022, Wageningen Academic Publishers, p. 481-486. https://doi.org/10.3920/978-90-8686-939-8_75.
Giersberg, M.F., Meijboom, F.L.B. (2021). Smart technologies lead to smart answers? On the claim of smart sensing technologies to tackle animal related societal concerns in Europe over current pig husbandry systems. Frontiers in Veterinary Science 7. https://doi.org/10.3389/fvets.2020.588214.
