A Model-Based Deep-Learning Approach to Reconstructing the Highly Articulated Flight Kinematics of Bats

A Model-Based Deep-Learning Approach to Reconstructing the Highly Articulated Flight Kinematics of Bats | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Applied AI Letters This is a preprint and has not been peer reviewed. Data may be preliminary. 6 January 2025 V1 Latest version Share on A Model-Based Deep-Learning Approach to Reconstructing the Highly Articulated Flight Kinematics of Bats Authors : Yihao Hu 0009-0002-8018-4059 [email protected] and Rolf Müller Authors Info & Affiliations https://doi.org/10.22541/au.173615067.75021762/v1 Published Applied AI Letters Version of record Peer review timeline 770 views 304 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share 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 efforts. 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 silhouette 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 4,975 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. Supplementary Material File (revised_applied_ai_letter.pdf) Download 12.36 MB Information & Authors Information Version history V1 Version 1 06 January 2025 Peer review timeline Published Applied AI Letters Version of Record 8 Jun 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Applied AI Letters Keywords articulated model bat maneuver kinematic reconstruction Authors Affiliations Yihao Hu 0009-0002-8018-4059 [email protected] Virginia Polytechnic Institute and State University Bradley Department of Electrical and Computer Engineering View all articles by this author Rolf Müller Virginia Polytechnic Institute and State University Department of Mechanical Engineering View all articles by this author Metrics & Citations Metrics Article Usage 770 views 304 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Yihao Hu, Rolf Müller. A Model-Based Deep-Learning Approach to Reconstructing the Highly Articulated Flight Kinematics of Bats. Authorea . 06 January 2025. DOI: https://doi.org/10.22541/au.173615067.75021762/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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