KiMA: Kinematic Motion Analysis for Spinal Cord Injury Research

Accurate quantification of locomotor recovery is essential for evaluating therapeutic outcomes in spinal cord injury (SCI) models. Manual scoring systems remain observer-dependent, and commercial gait-analysis platforms are costly and proprietary. Markerless pose-estimation tools such as DeepLabCut generate accurate body-part coordinates, but converting these coordinates into biologically meaningful locomotor parameters typically requires custom programming and multiple external tools. We developed KiMA (Kinematic Motion Analysis), an open-source, browser-based suite for integrated analysis of rodent gait and hindlimb kinematics. KiMA accepts DeepLabCut coordinate files and performs automated coordinate parsing, stick-figure reconstruction, frame-by-frame movement inspection, and single- and multi-sample analysis, with dedicated workflows for ladder and rung analysis, footfall detection, and CatWalk gait analysis. The platform quantifies joint angles (metatarsophalangeal, ankle, knee, hip, and pelvic), stride length, stride width, cadence, stance and swing durations, paw-contact events, swing clearance, and locomotor symmetry, and supports cohort-level comparisons, correlation analysis, principal component analysis, and export of processed datasets and publication-quality figures. Because KiMA runs entirely within a standard web browser, it requires no software installation or local programming environment, supporting cross-platform accessibility and data privacy. By unifying gait quantification, visualization, and multivariate analysis in a single interface, KiMA lowers the computational barrier to markerless locomotor analysis and helps researchers detect subtle functional recovery after SCI.