An Application for Automated Drosophila Locomotor Assay with Integrated Device Design and Computer Vision Tracking

Drosophila has long served as a powerful model for investigating locomotor behavior, and geotaxis assays have generated valuable insights into genetics, aging, and neurobiology. Nonetheless, their use can be constrained by subjective scoring, modest throughput, and challenges in reproducibility. To complement and extend these classical approaches, 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 nearly 800-fold higher data density. Application of the platform uncovered reproducible phenotypes of multiple genotypes. For example, a circadian mutant known as Clockout, males displayed progressive climbing deficits with age, whereas females-maintained age-resilient trajectories. Moreover, male Clockout exhibited a reduced performance compared to age-matched control (w1118), however, female Clockout showed subtle reduction in 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, while males exhibited 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.