Design and Implementation of an Automated Drosophila Locomotor Assay Using Computer Vision Tracking

Design and Implementation of an Automated Drosophila Locomotor Assay Using Computer Vision Tracking | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Design and Implementation of an Automated Drosophila Locomotor Assay Using Computer Vision Tracking Dave Melkani, Neelaksh Harnwal, Shubhankar Desai, Dev Patel, Girish Melkani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8769384/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract Drosophila has long served as a powerful model for investigating locomotor behavior, using geotaxis assays to generate valuable insights into genetics, aging, and neurobiology. Nonetheless, mostly their use can be constrained by subjective scoring, modest thought, and challenges in reproducibility. We developed and validated an integrated hardware–software platform that enables automated, high-resolution locomotor analysis across 12 vials in parallel. The system integrates 3D-printed mechanical components, Raspberry Pi–based video acquisition, and programmable environmental controls to ensure standardized conditions. A deep learning pipeline segments vials with near-perfect accuracy (IoU > 0.95), while computer vision algorithms quantify climbing trajectories, velocity, and positional zone occupancy at 60 frames per second. The end-to-end workflow converts raw video into time-resolved metrics, supports sex-specific aggregation, and incorporates advanced statistical analyses, including Linear Mixed Effects regression, harmonic mean p-values, and Mann–Whitney U tests. Relative to manual scoring, this automated pipeline yields 2.8-fold faster processing and about 800-fold higher data density. Application of the platform uncovered reproducible phenotypes of multiple genotypes. For example, in circadian Clockᴼᵘᵗ mutation, males displayed progressive climbing deficits with age, whereas females-maintained age-resilient trajectories. Moreover, male Clockᴼᵘᵗ exhibited a reduced locomotor performance compared to age-matched control ( w 1118 ) males, however, female Clockᴼᵘᵗ showed subtle reduction in locomotor performance. Additionally, glial-specific knockdown of PolG , encoding the DNA polymerase gamma catalytic subunit, revealed striking sex-dimorphic aging patterns: females outperformed controls at older age in Glaz driven and at younger age in Elav driven, while males exhibited some marked decline. To promote broad adoption, a user-friendly Python interface (Tkinter GUI) enables accessibility independent of computational expertise. Collectively, this standardized, high-throughput framework advances the resolution of genotype-, age-, and sex-dependent locomotor dynamics, offering new opportunities in aging, circadian biology, and neurodegeneration research. Biological sciences/Biological techniques Biological sciences/Computational biology and bioinformatics Biological sciences/Neuroscience Computer vision pipeline Deep learning–based segmentation Automated locomotor analysis High-throughput behavioral phenotyping Drosophila Model Full Text Additional Declarations No competing interests reported. Supplementary Files SIMachineLearningDrosophilaLocomotorAssay.pdf Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 01 Apr, 2026 Reviews received at journal 29 Mar, 2026 Reviews received at journal 29 Mar, 2026 Reviews received at journal 10 Mar, 2026 Reviewers agreed at journal 07 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviewers agreed at journal 05 Mar, 2026 Reviews received at journal 23 Feb, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviewers invited by journal 09 Feb, 2026 Editor assigned by journal 09 Feb, 2026 Editor invited by journal 09 Feb, 2026 Submission checks completed at journal 08 Feb, 2026 First submitted to journal 08 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8769384","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":588760175,"identity":"c098410d-a4ba-4450-be4f-d384293c423b","order_by":0,"name":"Dave Melkani","email":"","orcid":"","institution":"University of Alabama at Birmingham","correspondingAuthor":false,"prefix":"","firstName":"Dave","middleName":"","lastName":"Melkani","suffix":""},{"id":588760176,"identity":"94e77e5b-039f-4d01-aa7a-c62aab9171c8","order_by":1,"name":"Neelaksh Harnwal","email":"","orcid":"","institution":"University of Alabama at 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Additionally, glial-specific knockdown of \u003cem\u003ePolG\u003c/em\u003e, encoding the DNA polymerase gamma catalytic subunit, revealed striking sex-dimorphic aging patterns: females outperformed controls at older age in Glaz driven and at younger age in Elav driven, while males exhibited some marked decline. To promote broad adoption, a user-friendly Python interface (Tkinter GUI) enables accessibility independent of computational expertise. 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