Lameness is one of the costliest pathological problems affecting dairy animals. It is usually assessed by trained veterinary clinicians who observe features such as gait symmetry or gait parameters as step counts in real-time. With the development of artificial intelligence, various modular systems have been proposed to minimize subjectivity in lameness assessment. However, the major limitation in their development is the unavailability of a public dataset which is currently either commercial or privately held. To tackle this limitation, we have introduced CowScreeningDB which was created using sensory data. This dataset was sourced from 43 cows at a dairy located in Gran Canaria, Spain. It consists of a multi-sensor dataset built on data collected using an Apple Watch 6 during the normal daily routine of a dairy cow. Thanks to the collection environment, sampling technique, information regarding the sensors, the applications used for data conversion and storage make the dataset a transparent one. This transparency of data can thus be used for further development of techniques for lameness detection for dairy cows which can be objectively compared. Aside from the public sharing of the dataset, we have also shared a machine-learning technique which classifies the caws in healthy and lame by using the raw sensory data. Hence validating the major objective which is to establish the relationship between sensor data and lameness.

翻译：跛行是影响奶牛的最为昂贵的病理问题之一。通常由经过培训的兽医临床医生通过实时观察步态对称性或步态参数（如步数）等特征进行评估。随着人工智能的发展，人们提出了各种模块化系统，以最大限度地减少跛行评估中的主观性。然而，其发展的主要限制在于缺乏公开可用的数据集，目前这些数据要么是商业性的，要么是私人持有的。为了解决这一限制，我们引入了利用传感器数据创建的CowScreeningDB。该数据集来源于西班牙大加那利岛一家奶牛场的43头奶牛。它是一个多传感器数据集，基于使用Apple Watch 6在奶牛日常正常活动中收集的数据构建。得益于收集环境、采样技术、传感器相关信息以及用于数据转换和存储的应用程序，该数据集具有透明性。这种数据透明度因此可用于进一步开发奶牛跛行检测技术，并可以进行客观比较。除了公开共享数据集外，我们还分享了一种机器学习技术，该技术利用原始传感器数据将奶牛分类为健康或跛行。从而验证了主要目标，即建立传感器数据与跛行之间的关系。