Evaluation of the tri-axial accelerometer to identify and predict parturition-related activities of Debouillet ewes in an intensive setting
Evaluation of the tri-axial accelerometer to identify and predict parturition-related activities of Debouillet ewes in an intensive setting
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DOI：
10.1016/j.applanim.2021.105296
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发表时间：
2021-03-19
影响因子：
2.3
通讯作者：
Gifford, Jennifer A. Hernandez
中科院分区：
文献类型：
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作者：
Gurule, Sara C.;Tobin, Colin T.;Gifford, Jennifer A. Hernandez
识别和监测单个动物的分娩可能有助于生产者提高注意力，从而能够在分娩期间及早发现难产。分娩事件的特点是细微的行为变化，通常仅靠观察很难发现。这项研究的目的是确定三轴加速度计数据在围栏环境中准确识别和预测成熟母羊分娩相关行为的能力。将记录频率为12.5赫兹的三轴加速度计放置在耳标上，并在分娩前安装在13只德布依勒成熟母羊身上。分别在产羔前7天(d-7)、产羔当天(D0)和产羔后7天(d+7)进行活动监测。使用随机森林机器学习，加速度计数据和视觉观察被用来预测(I)七个相互排斥的行为；以及(Ii)基于使用加速度计记录的运动变化计算的五个指标的活动(活动和不活动行为)。从一个独立的验证集预测的7个行为的准确率为66.7%，活动的准确率为87.2%。除了预测的行为和活动外，根据加速计数据计算的用于随机森林预测的指标在产羔前和产羔后7d以及产羔前和产羔后12h对13只母羊中的6只进行了评估，其中6只母羊观察了实际产羔时间。在产羔前和产羔后的七种预测行为上没有发现差异。5个加速度计指标中有4个(P<；0.002)在产羔后7天内高于产羔前7天。五个指标中有三个指标的值在产羔日最高(P<；0.01)。在分娩前12小时和产后12小时期间，所有五个加速度计指标都不同(P<；0.004)。所有加速度计指标均在临产前1~2 h较临产前3~4 h升高(P<；0.008)。在这项目前的研究中，通过复杂的机器学习算法处理，计算的直接传感器指标作为羊羔行为的更好指标，比预测的行为更好。加速计的商业化使用可以检测到分娩过程中可能出现难产的延长分娩时间，从而降低羔羊死亡率和提高生产效率。这些结果表明，实时加速计可以远程监测绵羊，并有可能为管理人员提供大坝可能很快就会形成的迹象。
Identifying and monitoring parturition of individual animals may help producers increase attentiveness, enabling early detection of dystocia during parturition. Parturition events are marked by subtle behavioral changes often difficult to detect by observation alone. The aim of this study was to determine the ability of tri-axial accelerometer data to accurately identify and predict parturition-related behavior of mature ewes in a pen setting. Triaxial accelerometers recording at 12.5 Hz were placed on ear tags and attached to 13 Debouillet mature ewes before parturition. Activity was monitored 7 days prior to lambing (d -7); on the day of lambing (d 0); and 7 days post lambing (d +7). Using random forest machine learning, accelerometer data and visual observations were used to predict (i) seven mutually-exclusive behaviors; and (ii) activity (active and inactive behavior) based on five metrics calculated using variation of movements recorded by the accelerometer. The accuracy of seven predicted behaviors from an independent validation set was 66.7 %, and the accuracy for activity was 87.2 %. In addition to predicted behavior and activity, metrics calculated from accelerometer data and used for random forest predictions were evaluated 7 d before and after lambing and 12 h before and after lambing on six of the 13 ewes where the actual time of lambing was observed. No differences were detected in the seven predicted behaviors either before or after lambing. Four of five accelerometer metrics (P < 0.002) were higher during the 7 d after lambing than the 7 d before lambing. Values for three of the five metrics were highest (P < 0.01) on the day of lambing. All five accelerometer metrics varied during the 12 h pre- and 12 h post parturition (P < 0.004). All accelerometer metrics increased (P < 0.008) 1?2 h before parturition compared to 3?4 h before parturition. In this current study, calculated direct sensor metrics served as a better indicator of lambing than predicted behaviors, processed through complex machine learning algorithms. Commercial use of accelerometers by producers may allow for detection of prolonged labor indicating potential dystocia during parturition, which may reduce lamb mortality and increase production efficiency. These results suggest that real time accelerometers could remotely monitor ewes and potentially provide managers an indication that the dam may lamb soon.