This paper explores the innovative use of simulation environments to enhance data acquisition and diagnostics in veterinary medicine, focusing specifically on gait analysis in dogs. The study harnesses the power of Blender and the Blenderproc library to generate synthetic datasets that reflect diverse anatomical, environmental, and behavioral conditions. The generated data, represented in graph form and standardized for optimal analysis, is utilized to train machine learning algorithms for identifying normal and abnormal gaits. Two distinct datasets with varying degrees of camera angle granularity are created to further investigate the influence of camera perspective on model accuracy. Preliminary results suggest that this simulation-based approach holds promise for advancing veterinary diagnostics by enabling more precise data acquisition and more effective machine learning models. By integrating synthetic and real-world patient data, the study lays a robust foundation for improving overall effectiveness and efficiency in veterinary medicine.

使用仿真环境进行数据采集和诊断的创新性研究，特别关注于犬只的步态分析。该研究利用Blender和Blenderproc库生成反映不同解剖学、环境和行为条件的合成数据集。通过图形表示和标准化以实现最佳分析，利用生成的数据集来训练机器学习算法以识别正常和异常的步态。创建了两个具有不同相机角度粒度的不同数据集，以进一步研究相机视角对模型准确性的影响。初步结果表明，这种基于仿真的方法有望通过实现更精确的数据采集和更有效的机器学习模型来推动兽医诊断的发展。通过整合合成和现实世界的患者数据，该研究为提高兽医医学的整体效果和效率奠定了坚实的基础。

利用模拟校准兽医医学实际数据采集