Tracked keypoints of walking flies used in: Miniature linear and split-belt treadmills reveal mechanisms of adaptive motor control in walking Drosophila

To navigate complex environments, walking animals must detect and overcome unexpected perturbations. One technical challenge when investigating adaptive locomotion is measuring behavioral responses to precise perturbations during naturalistic walking; another is that experimentally silencing neurons in sensorimotor circuits often reduces spontaneous locomotion. To overcome these obstacles, we introduce miniature treadmill systems for coercing locomotion and tracking 3D kinematics of walking Drosophila. By systematically comparing walking in three experimental setups, we show that flies compelled to walk on the linear treadmill have similar stepping kinematics to freely walking flies, while kinematics of tethered walking flies are subtly different. Genetically silencing mechanosensory neurons alters step kinematics of flies walking on the linear treadmill across all speeds, while inter-leg coordination remains intact. We also found that flies can maintain a forward heading on a split-bel..., The datasets within this repository were collected by recording flies with high-speed cameras walking on a mini treadmill (linear or split-belt treadmill), freely in a featureless arena, or on a ball suspended in air while tethered. Flies on the linear treadmill and split-belt treadmills were recorded with 5 high-speed cameras at 180 and 200 fps, respectively. Flies walking in the arena were recorded at 150 fps with a top-down camera. Lastly, tethered flies walking on the ball were recorded with 6 high-speed cameras at 300 fps. We then used modern pose estimation tools (DeepLabCut and Anipose for treadmill and tethered walking flies, and SLEAP for freely walking flies) to extract 2D or 3D positions for labeled key points (i.e. points on the body and leg tips). We then computed walking kinematics from these positions using Python. Please refer to the corresponding paper for more details on data collection and processing. , , # Kinematic datasets of treadmill, freely, and tethered walking flies

### [https://doi.org/10.5061/dryad.mpg4f4r73](https://doi.org/10.5061/dryad.mpg4f4r73)

#### Overview

The datasets within this repository contain the tracked keypoints of treadmill (linear and split-belt), freely, and tethered walking flies. In figures 1-3 of the paper, wild-type berlin flies were used in linear treadmill, freely walking, and tethered experiments. Experiments consisted of recording flies walking in those setups with high-speed cameras and extracting 2D (freely walking flies) or 3D (treadmill and tethered walking flies) positions using modern pose estimation tools (DeepLabCut and Anipose, and SLEAP, respectively). Walking kinematics were then calculated from these positions in Python. The Python scripts were designed to run the data files located in this repository and are located in the following GitHub repo: [https://github.com/Prattbuw/Treadmill_Paper](https://github.com/Prattbuw/Treadmill_Paper)....

为在复杂环境中行进，行走类动物必须感知并克服突发扰动。在研究适应性运动时，一项核心技术挑战在于：如何在自然行走状态下，精准量化动物对可控扰动的行为响应；另一项挑战则是，通过实验沉默感觉运动回路（sensorimotor circuits）中的神经元时，往往会抑制动物的自发运动。为突破上述障碍，我们开发了可强制果蝇（Drosophila）行走并追踪其三维运动学特征的微型跑步机系统。通过系统对比三种实验范式下的果蝇行走行为，我们发现：被迫在直线跑步机上行进的果蝇，其步行动力学特征与自由行走的果蝇高度相似；而悬挂行走果蝇的运动学特征则存在细微差异。对机械感觉神经元进行遗传沉默后，直线跑步机上各行走速度下的果蝇步行动力学均发生改变，但腿间协调模式仍保持完整。我们还发现，果蝇可在分离带跑步机（split-belt treadmill）上维持前进航向……

本仓库内的数据集通过以下方式采集：利用高速摄像机（high-speed camera）记录在微型跑步机（直线型或分离带型）上行进的果蝇、在无特征环境中自由行走的果蝇，以及被悬挂于空中的球体上行进的果蝇。直线跑步机与分离带跑步机上的果蝇分别以180 fps与200 fps的帧率，通过5台高速摄像机录制；开放环境中自由行走的果蝇以150 fps的帧率通过顶置摄像机录制；最后，悬挂于球体上行进的果蝇以300 fps的帧率通过6台高速摄像机录制。随后，我们采用现代化姿态估计工具（pose estimation tools）——针对跑步机与悬挂行走果蝇使用DeepLabCut与Anipose，针对自由行走果蝇使用SLEAP——提取标注关键点（即躯体与腿部尖端的点位）的二维或三维坐标。基于这些坐标，我们通过Python计算得到运动学参数。有关数据采集与处理的更多细节，请参阅对应研究论文。

# 跑步机、自由行走与悬挂行走果蝇的运动学数据集
### [https://doi.org/10.5061/dryad.mpg4f4r73](https://doi.org/10.5061/dryad.mpg4f4r73)
#### 概述
本仓库内的数据集包含跑步机（直线型与分离带型）、自由行走以及悬挂行走果蝇的追踪关键点数据。在本文的图1至图3中，野生型柏林品系果蝇（wild-type Berlin flies）被用于直线跑步机、自由行走与悬挂行走相关实验。实验流程为：通过高速摄像机记录上述三种范式下的果蝇行走行为，再分别采用现代化姿态估计工具（DeepLabCut与Anipose，以及SLEAP）提取得到二维（自由行走果蝇）或三维（跑步机与悬挂行走果蝇）坐标。随后通过Python从这些坐标中计算得到运动学参数。本仓库附带的Python脚本可直接处理本仓库内的数据文件，脚本托管于以下GitHub仓库：[https://github.com/Prattbuw/Treadmill_Paper](https://github.com/Prattbuw/Treadmill_Paper)……