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ABSTRACT Bats are capable of highly dexterous flight maneuvers that rely heavily on highly articulated hand skeletons and malleable wing membranes. To understand the underlying mechanisms, large amounts of detailed data on bat flight kinematics are required. Conventional methods to obtain these data have been based on tracing landmarks and require substantial manual effort. To generate 3D reconstructions of the entire geometry of a flying bat in a fully automated fashion, the current work has developed an approach where the pose of a trainable articulated mesh template that is based on the bat's anatomy is optimized to fit a set of binary silhouettes representing views from different directions of the flying bat. This is followed by post‐processing to smooth the reconstructed kinematics and simulate the non‐rigid motion of the wing membranes. To evaluate the method, 10 flight sequences that represent several flight maneuvers (e.g., straight flight, takeoff, u‐turn) and were recorded in a flight tunnel instrumented with 50 synchronized cameras have been reconstructed. A total of 4975 reconstructions are generated in this fashion and subject to qualitative and quantitative evaluations with promising results. The reconstructions are to be used for quantitative analyses of the maneuvering kinematics and the associated aerodynamics.
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Application of a novel deep learning–based 3D videography workflow to bat flight
Abstract Studying the detailed biomechanics of flying animals requires accurate three‐dimensional coordinates for key anatomical landmarks. Traditionally, this relies on manually digitizing animal videos, a labor‐intensive task that scales poorly with increasing framerates and numbers of cameras. Here, we present a workflow that combines deep learning–powered automatic digitization with filtering and correction of mislabeled points using quality metrics from deep learning and 3D reconstruction. We tested our workflow using a particularly challenging scenario: bat flight. First, we documented four bats flying steadily in a 2 m3wind tunnel test section. Wing kinematic parameters resulting from manually digitizing bats with markers applied to anatomical landmarks were not significantly different from those resulting from applying our workflow to the same bats without markers for five out of six parameters. Second, we compared coordinates from manual digitization against those yielded via our workflow for bats flying freely in a 344 m3enclosure. Average distance between coordinates from our workflow and those from manual digitization was less than a millimeter larger than the average human‐to‐human coordinate distance. The improved efficiency of our workflow has the potential to increase the scalability of studies on animal flight biomechanics.
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- Award ID(s):
- 1931200
- PAR ID:
- 10501990
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Annals of the New York Academy of Sciences
- Volume:
- 1536
- Issue:
- 1
- ISSN:
- 0077-8923
- Format(s):
- Medium: X Size: p. 92-106
- Size(s):
- p. 92-106
- Sponsoring Org:
- National Science Foundation
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