Data from: Automated reconstruction of three-dimensional fish motion, forces, and torques

Fish can move freely through the water column and make complex three-dimensional motions to explore their environment, escape or feed. Nevertheless, the majority of swimming studies is currently limited to two-dimensional analyses. Accurate experimental quantification of changes in body shape, position and orientation (swimming kinematics) in three dimensions is therefore essential to advance biomechanical research of fish swimming. Here, we present a validated method that automatically tracks a swimming fish in three dimensions from multi-camera high-speed video. We use an optimisation procedure to fit a parameterised, morphology-based fish model to each set of video images. This results in a time sequence of position, orientation and body curvature. We post-process this data to derive additional kinematic parameters (e.g. velocities, accelerations) and propose an inverse-dynamics method to compute the resultant forces and torques during swimming. The presented method for quantifying 3D fish motion paves the way for future analyses of swimming biomechanics.鱼类可在水层中自由穿梭，并通过复杂的三维运动探索环境、躲避敌害或觅食。然而当前绝大多数鱼类游动研究仍局限于二维分析范畴。因此，精准实验量化三维空间内鱼类的体型、位置与姿态变化——即游动运动学（swimming kinematics）——对于推动鱼类游动生物力学研究至关重要。本研究提出一种经实验验证的方法，可通过多相机高速视频自动追踪三维空间内的游动鱼类。该方法通过优化流程，将参数化的基于形态学的鱼类模型适配至每一组视频图像帧，最终得到包含位置、姿态与身体曲率的时间序列数据集。研究团队通过对该数据集进行后处理，提取速度、加速度等额外运动学参数，并提出逆动力学方法，用于计算鱼类游动过程中的合外力与合外力矩。本研究提出的三维鱼类运动量化方法，为未来鱼类游动生物力学的相关研究铺平了道路。