Authors - D. P. Jayathung, M. Ramashini, Juliana Zaini, R. Muller, Liyanage C. De Silva Abstract - The primary objective of this research is to explore and interpret the complex flight kinematics of bats in order to deepen aerodynamic understanding and inspire future technological innovation. To achieve this, the study adopts a hybrid approach for estimating flapping pose phases in high-speed bat flight recordings. Accurately distinguishing between the upstroke and downstroke phases is essential for examining the subtle dynamics and movement patterns of bats’ uniquely flexible wing structures. The methodology followed a structured work-flow, beginning with video acquisition using an array of 50 high-speed cameras that recorded bat flights at 1000 frames per second within a controlled tunnel environment. An enhanced YOLOv5L model was then employed to remove un-necessary frames, achieving a mean Average Precision (mAP) of 99.3% and successfully filtering out more than 85% of unwanted footage. For the pose estimation, this work used DeepLabCut to define 20 anatomical keypoints. After com-paring five backbone architectures, this study selected ResNet50 as the most suit-able model, as it yielded the lowest test RMSE (3.98) and the highest test mAP (97.62%). A rule-based geometric method was developed to classify bat wing-beat phases using elbow–wrist–wingtip angles derived from DeepLabCut key-points. By analyzing the smoothed angle trajectory and its temporal derivative, the rule-based approach reliably identified upstroke and downstroke cycles, which were validated using test videos. The extracted phase information supports a deeper biomechanical understanding of bat flight while also enabling applications in bio-inspired robotics, real-time flight monitoring, and automated analysis of complex animal motion.
