Deep-Learning Based Multi-Joint Synchronous Tracking for Objective Quantification of Hindlimb Locomotor Kinematics in Rats.

Accurate and objective assessment of rat gait is essential to neuroscience and kinesiology research fields. Conventional systems frequently depend on footprint imaging for indirect gait inference, which cannot fully capture hindlimb multi-joint kinematics. This study establishes a markerless treadmill-based gait analysis system for rodents, integrating custom deep learning algorithms with a programmable weight-support treadmill to enable real-time tracking of multiple lower-limb joints and multidimensional kinematic quantification. The system automatically extracts gait cycle parameters, joint trajectories, force distribution patterns, and movement smoothness under modulated speed (0-300 mm/s), incline (±30° gradient), and graded weight support (0-500 g) conditions, providing an objective assessment tool for neuromuscular behavior research. Using spinal cord injury (SCI) models, results demonstrate the system's sensitivity in detecting multidimensional differences in joint range of motion, trajectory continuity, propulsive force output, and movement smoothness between healthy and injured rodents, validating its disease discrimination and grading capabilities. Compared to traditional subjective scores or footprint methods, this platform eliminates personal bias and low-dimensional data limitations while combining deep learning-driven architecture and high-throughput data collection advantages. It is applicable to research involving central or peripheral nerve injury, neurodegenerative diseases, musculoskeletal disorders, and aging processes. Through synchronous integration of the neuroelectrophysiology modules, the system further enables temporal coupling of gait and neural signals, establishing a methodological framework for parsing central-peripheral control mechanisms and developing neuromodulatory strategies.