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、躲避敌害或摄食。然而当前绝大多数鱼类游泳研究仍局限于二维分析范畴。因此，对鱼类三维体态、位置与姿态（游泳运动学，swimming kinematics）的变化进行精准实验量化，对推动鱼类游泳生物力学研究至关重要。本研究提出一种经验证的方法，可基于多摄像头高速视频自动追踪三维空间内的鱼类游泳行为。我们通过优化流程，将基于形态学的参数化鱼类模型拟合至每一组视频图像。由此可得到鱼类位置、姿态与体曲率的时间序列数据。我们对该数据进行后处理以提取额外运动学参数（如速度、加速度），并提出一种逆动力学方法以计算鱼类游泳过程中的合力与力矩。本研究提出的三维鱼类运动量化方法，为未来鱼类游泳生物力学研究分析铺平了道路。