{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T08:32:05Z","timestamp":1793781125200,"version":"4.6.1"},"posted":{"date-parts":[[2026,3,31]]},"group-title":"Animal Behavior and Cognition","reference-count":18,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:00:00Z","timestamp":1774915200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.biorxiv.org\/about\/FAQ#license"}],"funder":[{"award":["1R01NS136988"],"award-info":[{"award-number":["1R01NS136988"]}],"id":[{"id":"https:\/\/ror.org\/01cwqze88","id-type":"ROR","asserted-by":"publisher"}]},{"award":["2112085"],"award-info":[{"award-number":["2112085"]}],"id":[{"id":"https:\/\/ror.org\/021nxhr62","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,11,3]]},"abstract":"<jats:p>Body yaw orientation is a key variable in the analysis of insect flight behavior, yet it remains difficult to measure across the full extent of a trajectory in most experimental settings. Although modern tracking systems reliably capture the position and velocity of the center of mass, resolving body yaw orientation typically requires dedicated hardware confined to a small, purpose-built volume, and is impractical for large-scale or long-duration studies. Here, we develop a data-driven estimator that predicts body yaw orientation for Drosophila directly from translational flight trajectory data. We trained a fully connected feedforward artificial neural network (ANN) on a dataset in which both flight trajectory and body yaw orientation were recorded simultaneously in freely flying Drosophila, using ground velocity and air velocity vectors as input features. We show that the ANN predicts body yaw orientation with a median error of 5.0 degrees, and substantially outperforms linear and Mobius regressions when flies are moving crosswind or downwind. The estimator may be a practical tool for recovering body yaw orientation information from existing trajectory datasets in which only center-of-mass motion was recorded, extending the behavioral and computational analysis of insect navigation to previously inaccessible data.<\/jats:p>","DOI":"10.64898\/2026.03.30.715335","type":"posted-content","created":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T19:00:39Z","timestamp":1774983639000},"source":"Crossref","is-referenced-by-count":2,"title":["Predicting Drosophila Body Orientation from a Translational Trajectory using an Artificial Neural Network"],"prefix":"10.64898","author":[{"given":"Nehal","family":"Mangat","sequence":"first","affiliation":[{"name":"University of Nevada, Reno;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christina E","family":"May","sequence":"additional","affiliation":[{"name":"New York University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Floris","family":"van Breugel","sequence":"additional","affiliation":[{"name":"University of Nevada, Reno;"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"key":"2026063002551043000_2026.03.30.715335v2.1","doi-asserted-by":"publisher","DOI":"10.1098\/rsif.2010.0230"},{"key":"2026063002551043000_2026.03.30.715335v2.2","doi-asserted-by":"publisher","DOI":"10.1126\/scirobotics.adm7689"},{"key":"2026063002551043000_2026.03.30.715335v2.3","doi-asserted-by":"publisher","DOI":"10.1242\/jeb.118422"},{"key":"2026063002551043000_2026.03.30.715335v2.4","doi-asserted-by":"publisher","DOI":"10.1016\/j.cub.2013.12.023"},{"key":"2026063002551043000_2026.03.30.715335v2.5","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0129657"},{"key":"2026063002551043000_2026.03.30.715335v2.6","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0132101"},{"key":"2026063002551043000_2026.03.30.715335v2.7","doi-asserted-by":"publisher","DOI":"10.1126\/science.1248955"},{"key":"2026063002551043000_2026.03.30.715335v2.8","doi-asserted-by":"publisher","DOI":"10.64898\/2025.12.02.691855"},{"key":"2026063002551043000_2026.03.30.715335v2.9","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1005969"},{"key":"2026063002551043000_2026.03.30.715335v2.10","doi-asserted-by":"publisher","DOI":"10.1242\/jeb.01612"},{"issue":"83","key":"2026063002551043000_2026.03.30.715335v2.11","first-page":"1","article-title":"CVXPY: A Python-embedded modeling language for convex optimization","volume":"17","year":"2016","journal-title":"Journal of Machine Learning Research"},{"key":"2026063002551043000_2026.03.30.715335v2.12","unstructured":"MOSEK ApS. 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