PG-data

This study introduces a novel approach for the early detection of induced colic in horses through the use of accelerometer devices. By inducing transient colic in experimental mares and collecting accelerometric data, we developed a multi-phase strategy to identify episodes of colic and categorize their severity. The model utilizes both behavioral and activity index characteristics to detect the presence of colic and assess its severity. Our results demonstrate high accuracy in distinguishing between normal and pain-related behaviors, offering significant potential for non-invasive, continuous monitoring of horse health, and potentially faster, more effective treatment of colic. The main limitation is the inclusion of a limited number of horses with severe pain-related behaviors in the dataset.本研究提出了一种借助加速度计（accelerometer）设备早期检测诱发性马疝痛的创新方案。研究团队通过对实验母马诱发暂时性疝痛并采集加速度数据（accelerometric data），开发了一套多阶段策略，用于识别疝痛发作并对其严重程度进行分级。该模型融合行为特征与活动指数特征，实现疝痛的检出与严重程度评估。研究结果表明，模型在区分正常行为与疼痛相关行为方面拥有高精度表现，可为马类健康的无创连续监测提供重大应用潜力，亦有望推动疝痛诊疗的更快响应与更高效实施。本研究的主要局限在于，数据集中纳入的表现出重度疼痛相关行为的马匹数量较为有限。