Behavioural compass: animal behaviour recognition using magnetometers
Behavioural compass: animal behaviour recognition using magnetometers
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DOI：
10.1186/s40462-019-0172-6
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发表时间：
2019-08-27
期刊：
影响因子：
4.1
通讯作者：
Aminian, Kamiar
中科院分区：
文献类型：
--
作者：
Chakravarty, Pritish;Maalberg, Maiki;Aminian, Kamiar
背景当今的动物数据记录仪通常装有多个传感器，以高频率同时记录。这为从单个传感器以及集成的多传感器数据中获得对行为的精细洞察提供了机会。在行为识别的背景下，尽管加速度计已被广泛使用，但磁力计最近已被证明可以检测加速度计遗漏的特定行为。有限的训练数据的普遍约束使得识别对来自新个体的数据具有高鲁棒性的行为变得非常重要，并且可能需要融合来自这两个传感器的数据。然而，目前还没有研究开发出一种端到端的方法来识别常见的动物行为，如觅食，运动和休息，这些行为来自能够容纳和比较两个传感器数据的通用分类框架中的磁力计数据。方法我们首先利用磁力计与加速度计的相似性来开发运动的生物力学描述符：我们使用传感器倾斜相对于地球局部磁场给出的静态分量来估计姿势，以及传感器倾斜随时间变化给出的动态分量来估计运动强度和周期性。我们在现有的混合方案中使用这些描述符，该方案结合了生物力学和机器学习来识别行为。我们展示了我们的方法的效用三轴磁强计收集的数据上十野生喀拉哈里猫鼬（Suricata suricatta），每个人的注释视频记录作为地面实况。最后，我们将我们的结果与基于加速度计的行为识别进行比较。结果发现使用磁力计数据获得的> 94%的总体识别准确度与使用加速度计数据获得的识别准确度相当。有趣的是，磁力计对动态行为中个体间变化的鲁棒性更高，而加速度计在估计姿势方面更好。结论发现磁力计可以准确识别常见行为，并且对动态行为识别特别稳健。使用生物力学的考虑，以总结磁力计数据，使混合方案能够容纳来自同一框架内的任一个或两个传感器的数据，根据每个传感器的优势。这为未来的研究提供了一种方法来评估使用磁力计进行行为识别的额外好处。
Background Animal-borne data loggers today often house several sensors recording simultaneously at high frequency. This offers opportunities to gain fine-scale insights into behaviour from individual-sensor as well as integrated multi-sensor data. In the context of behaviour recognition, even though accelerometers have been used extensively, magnetometers have recently been shown to detect specific behaviours that accelerometers miss. The prevalent constraint of limited training data necessitates the importance of identifying behaviours with high robustness to data from new individuals, and may require fusing data from both these sensors. However, no study yet has developed an end-to-end approach to recognise common animal behaviours such as foraging, locomotion, and resting from magnetometer data in a common classification framework capable of accommodating and comparing data from both sensors. Methods We address this by first leveraging magnetometers' similarity to accelerometers to develop biomechanical descriptors of movement: we use the static component given by sensor tilt with respect to Earth's local magnetic field to estimate posture, and the dynamic component given by change in sensor tilt with time to characterise movement intensity and periodicity. We use these descriptors within an existing hybrid scheme that combines biomechanics and machine learning to recognise behaviour. We showcase the utility of our method on triaxial magnetometer data collected on ten wild Kalahari meerkats (Suricata suricatta), with annotated video recordings of each individual serving as groundtruth. Finally, we compare our results with accelerometer-based behaviour recognition. Results The overall recognition accuracy of > 94% obtained with magnetometer data was found to be comparable to that achieved using accelerometer data. Interestingly, higher robustness to inter-individual variability in dynamic behaviour was achieved with the magnetometer, while the accelerometer was better at estimating posture. Conclusions Magnetometers were found to accurately identify common behaviours, and were particularly robust to dynamic behaviour recognition. The use of biomechanical considerations to summarise magnetometer data makes the hybrid scheme capable of accommodating data from either or both sensors within the same framework according to each sensor's strengths. This provides future studies with a method to assess the added benefit of using magnetometers for behaviour recognition.