Direct monitoring of wild animals' behavior is challenging and data tampering. Instrument the animals with collars that embeds sensors, such as tri-axial accelerometer and GPS, allows obtaining sufficient information for remotely classifying the performed activities. In this work is presented an accurate and human intelligible framework, designed leveraging the authors' skills in machine-learning and data analysis. The system covers all the steps required to accurately map the raw signals to the activities carried out, grouped in pre- processing, features extraction and selection, and classification phases.

A case of study consists of a dataset collected by the Crofoot Lab at the Mpala Centre, in Kenya, instrumenting free-ranging Olive Baboons. This dataset provides both sensors time-series paired with respective activity labels and unlabeled ones. Labeled data was used to tune the parameters of the framework phases and to train and test the employed boosted- trees classifiers, while unlabeled ones were used for further system validations. The average accuracy obtained on a single activity is 94.5%. At best of the authors' knowledge, this is the first work that aims to solve the problem of direct human monitoring with such high accuracy, outperforming the state of the art by a lift about 10%.

The main contribution of the proposed systems consists of the attention paid to the ethologist's needs. Together with the predictions, the framework also returns a ranking for the features considered, based on their importance in the decision-making process of the classifier. Therefore, the extracted features are consistent with the logical human path that the ethologist follows in performing direct monitoring. The produced framework has also been designed consistently with the ethogram structure to be easily interpretable and to allow activities classification at different aggregation levels.

直接监测野生动物的行为具有挑战性，并且数据被篡改。使用嵌入传感器（例如三轴加速度计和GPS）的项圈来对动物进行仪表化，可以获取足够的信息以对执行的活动进行远程分类。在这项工作中，提出了一个准确且易于理解的框架，该框架是利用作者在机器学习和数据分析方面的技能而设计的。该系统涵盖了将原始信号准确映射到所执行的活动所需的所有步骤，这些活动分为预处理，特征提取和选择以及分类阶段。

一项研究案例由肯尼亚Mpala中心的Crofoot实验室收集的数据集组成，该数据集用于放养自由放养的橄榄狒狒。该数据集提供了与相应的活动标签和未标签的传感器配对的时间序列。标记数据用于调整框架阶段的参数，并训练和测试采用的升压树分类器，而未标记的则用于进一步的系统验证。一项活动获得的平均准确度为94.5％。据作者所知，这是第一个旨在以如此高的准确度解决直接人类监控问题的工作，其性能比现有技术高出约10％。

拟议系统的主要贡献包括对伦理学家需求的关注。框架与预测一起还根据所考虑的功能在分类器决策过程中的重要性返回所考虑功能的排名。因此，提取的特征与伦理学家在执行直接监视时遵循的逻辑人道一致。生成的框架还与人种统计结构进行了一致的设计，以易于解释，并允许在不同聚合级别进行活动分类。