Fontaine Ebraheem I, Zabala Francisco, Dickinson Michael H, Burdick Joel W
Mechanical Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
J Exp Biol. 2009 May;212(Pt 9):1307-23. doi: 10.1242/jeb.025379.
The fruit fly Drosophila melanogaster is a widely used model organism in studies of genetics, developmental biology and biomechanics. One limitation for exploiting Drosophila as a model system for behavioral neurobiology is that measuring body kinematics during behavior is labor intensive and subjective. In order to quantify flight kinematics during different types of maneuvers, we have developed a visual tracking system that estimates the posture of the fly from multiple calibrated cameras. An accurate geometric fly model is designed using unit quaternions to capture complex body and wing rotations, which are automatically fitted to the images in each time frame. Our approach works across a range of flight behaviors, while also being robust to common environmental clutter. The tracking system is used in this paper to compare wing and body motion during both voluntary and escape take-offs. Using our automated algorithms, we are able to measure stroke amplitude, geometric angle of attack and other parameters important to a mechanistic understanding of flapping flight. When compared with manual tracking methods, the algorithm estimates body position within 4.4+/-1.3% of the body length, while body orientation is measured within 6.5+/-1.9 deg. (roll), 3.2+/-1.3 deg. (pitch) and 3.4+/-1.6 deg. (yaw) on average across six videos. Similarly, stroke amplitude and deviation are estimated within 3.3 deg. and 2.1 deg., while angle of attack is typically measured within 8.8 deg. comparing against a human digitizer. Using our automated tracker, we analyzed a total of eight voluntary and two escape take-offs. These sequences show that Drosophila melanogaster do not utilize clap and fling during take-off and are able to modify their wing kinematics from one wingstroke to the next. Our approach should enable biomechanists and ethologists to process much larger datasets than possible at present and, therefore, accelerate insight into the mechanisms of free-flight maneuvers of flying insects.
果蝇黑腹果蝇是遗传学、发育生物学和生物力学研究中广泛使用的模式生物。将果蝇用作行为神经生物学模型系统的一个限制是,在行为过程中测量身体运动学既费力又主观。为了量化不同类型机动过程中的飞行运动学,我们开发了一种视觉跟踪系统,该系统可从多个校准相机估计果蝇的姿势。使用单位四元数设计了一个精确的几何果蝇模型,以捕捉复杂的身体和翅膀旋转,这些旋转会自动拟合到每个时间帧的图像中。我们的方法适用于一系列飞行行为,同时对常见的环境杂波也具有鲁棒性。本文使用该跟踪系统比较了自主起飞和逃避起飞过程中的翅膀和身体运动。使用我们的自动算法,我们能够测量冲程幅度、几何攻角以及对扑翼飞行的机械理解很重要的其他参数。与手动跟踪方法相比,该算法估计身体位置的误差在体长的4.4±1.3%以内,而身体方向的测量误差平均在六个视频中为6.5±1.9度(滚转)、3.2±1.3度(俯仰)和3.4±1.6度(偏航)。同样,冲程幅度和偏差估计误差在3.3度和2.1度以内,而与人工数字化仪相比,攻角通常测量误差在8.8度以内。使用我们的自动跟踪器,我们总共分析了八次自主起飞和两次逃避起飞。这些序列表明,黑腹果蝇在起飞过程中不使用拍击和甩动,并且能够在下一个翼冲程中改变其翅膀运动学。我们的方法应该能够使生物力学家和行为学家处理比目前可能的大得多的数据集,从而加速对飞行昆虫自由飞行机动机制的洞察。