Department of Biological Sciences, Boise State University, Boise, Idaho, United States of America.
Sky Patrol Abatement, Simi Valley, California, United States of America.
PLoS One. 2017 Apr 12;12(4):e0174785. doi: 10.1371/journal.pone.0174785. eCollection 2017.
Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data.
翱翔的鸟类可以通过在拍打、翱翔和滑翔飞行之间切换来平衡运动的能量成本。加速度计可以允许量化飞行行为，从而为解释这些能量成本提供背景。然而，用于解释加速度计数据的模型仍在开发中，很少使用有监督的数据集进行训练，并且难以应用。我们以 140Hz 的频率从一只受过训练的金鹰（Aquila chrysaetos）身上采集了加速度计数据，并用视频记录了它的飞行，我们用这些视频来描述它的行为。我们应用了两种形式的有监督分类，随机森林（RF）模型和 K-最近邻（KNN）模型。KNN 模型比 RF 方法更容易实现，但在分类拍打（分别为 85.5%和 83.6%准确）、翱翔（92.8%和 87.6%）和坐着（84.1%和 88.9%）等基本行为方面都非常准确，总体准确率分别为 86.6%和 92.3%。更详细的分类方案，如倾斜和直线飞行等特定行为，只有 KNN 模型才能很好地分类（准确率为 91.24%；RF 模型为 61.64%）。RF 模型在采样频率低至 10Hz 时仍能保持对基本行为分类的准确性，KNN 模型在采样频率低至 20Hz 时也能保持对基本行为分类的准确性。从自由放养的鸟类身上采集的加速度计数据的分类表明，预测行为强烈依赖于所使用的分类模型的类型。我们的分析表明了不同的加速度计数据分类方法的后果，有潜力通过验证飞行行为来优化分类算法，以提高分类准确性，确定不同分类算法的理想采样频率，以及改进常用的加速度计数据分类分析技术和最佳实践的几种方法。
