Movement activity based classification of animal behaviour with an application to data from cheetah (Acinonyx jubatus).
Movement activity based classification of animal behaviour with an application to data from cheetah (Acinonyx jubatus).
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DOI：
10.1371/journal.pone.0049120
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Hailes S
中科院分区：
文献类型：
--
作者：
Grünewälder S;Broekhuis F;Macdonald DW;Wilson AM;McNutt JW;Shawe-Taylor J;Hailes S
我们提出了一种新的方法，基于机器学习技术，从数据记录器和采样的同期行为观察的连续数据的组合进行分析。这种数据组合为生物学家提供了一个机会，使他们能够以以前未知的细节和准确性水平研究行为;然而，除非由此产生的大量原始数据能够可靠地转化为实际行为，否则连续记录的数据几乎没有用处。我们通过应用支持向量机和隐马尔可夫模型来解决这个问题，该模型允许我们使用一小组实地观察来对动物的行为进行分类，以校准连续记录的活动数据。这种分类数据可以定量地应用于动物在较长时期内的行为，以及在观察困难或不可能的时候。我们证明了该方法的实用性，将其应用到数据从六猎豹（Acinonyx jubatus）在奥卡万戈三角洲，博茨瓦纳。在平均一年左右的时间里，嵌入GPS无线电项圈的加速度计每五分钟记录一次累积活动数据分数。直接的行为采样的六个猎豹中的每一个在现场收集了相对较短的时间。使用这种方法，我们能够将每五分钟的活动评分分类为一组三个关键行为（喂养，移动的和静止），为整个部署项圈的时间段创建连续的行为序列。我们的分类与交叉验证的评估显示的准确性，但个别类的准确性降低直接观察的样本量减少。我们展示了如何处理这些数据可以用来研究行为，确定季节和性别差异的日常活动和喂养时间。这里给出的结果与使用传统方法在准确性和细节上都不同。
We propose a new method, based on machine learning techniques, for the analysis of a combination of continuous data from dataloggers and a sampling of contemporaneous behaviour observations. This data combination provides an opportunity for biologists to study behaviour at a previously unknown level of detail and accuracy; however, continuously recorded data are of little use unless the resulting large volumes of raw data can be reliably translated into actual behaviour. We address this problem by applying a Support Vector Machine and a Hidden-Markov Model that allows us to classify an animal's behaviour using a small set of field observations to calibrate continuously recorded activity data. Such classified data can be applied quantitatively to the behaviour of animals over extended periods and at times during which observation is difficult or impossible. We demonstrate the usefulness of the method by applying it to data from six cheetah (Acinonyx jubatus) in the Okavango Delta, Botswana. Cumulative activity data scores were recorded every five minutes by accelerometers embedded in GPS radio-collars for around one year on average. Direct behaviour sampling of each of the six cheetah were collected in the field for comparatively short periods. Using this approach we are able to classify each five minute activity score into a set of three key behaviour (feeding, mobile and stationary), creating a continuous behavioural sequence for the entire period for which the collars were deployed. Evaluation of our classifier with cross-validation shows the accuracy to be , but that the accuracy for individual classes is reduced with decreasing sample size of direct observations. We demonstrate how these processed data can be used to study behaviour identifying seasonal and gender differences in daily activity and feeding times. Results given here are unlike any that could be obtained using traditional approaches in both accuracy and detail.
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影响因子：
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Yamamoto, Takashi;Takahashi, Akinori;Trathan, Philip N.
通讯作者：
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作者：
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通讯作者：
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