Division of Robotics, Perception and Learning, KTH Royal Institute of Technology, Stockholm, Sweden.
Department of Anatomy, Physiology and Biochemistry, Swedish University of Agricultural Sciences, Uppsala, Sweden.
PLoS One. 2022 Mar 4;17(3):e0263854. doi: 10.1371/journal.pone.0263854. eCollection 2022.
Orthopedic disorders are common among horses, often leading to euthanasia, which often could have been avoided with earlier detection. These conditions often create varying degrees of subtle long-term pain. It is challenging to train a visual pain recognition method with video data depicting such pain, since the resulting pain behavior also is subtle, sparsely appearing, and varying, making it challenging for even an expert human labeller to provide accurate ground-truth for the data. We show that a model trained solely on a dataset of horses with acute experimental pain (where labeling is less ambiguous) can aid recognition of the more subtle displays of orthopedic pain. Moreover, we present a human expert baseline for the problem, as well as an extensive empirical study of various domain transfer methods and of what is detected by the pain recognition method trained on clean experimental pain in the orthopedic dataset. Finally, this is accompanied with a discussion around the challenges posed by real-world animal behavior datasets and how best practices can be established for similar fine-grained action recognition tasks. Our code is available at https://github.com/sofiabroome/painface-recognition.
骨科疾病在马中很常见，常导致马被实施安乐死，而如果早期发现这些疾病，往往可以避免安乐死。这些疾病常常导致不同程度的长期慢性疼痛。由于视频数据中所描绘的疼痛行为非常细微、稀疏且多变，因此很难通过视频数据训练出一种基于视觉的疼痛识别方法，即使是专业的人类标注员也很难为数据提供准确的真实标签。我们证明，仅在具有急性实验性疼痛的马的数据集上进行训练的模型（其中的标签不太模糊）可以帮助识别更细微的骨科疼痛表现。此外，我们还为该问题提供了人类专家的基准，并对各种领域转移方法以及在骨科数据集中经过干净的实验性疼痛训练的疼痛识别方法所检测到的内容进行了广泛的实证研究。最后，我们围绕真实动物行为数据集所带来的挑战以及如何为类似的精细动作识别任务建立最佳实践进行了讨论。我们的代码可在 https://github.com/sofiabroome/painface-recognition 上获得。
