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

Summary

This protocol establishes a rat hindlimb kinematic evaluation pipeline using a markerless treadmill test with deep learning-driven multi-joint trajectory auto-labeling, which enables reproducible motion quantification.

Abstract

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.

Introduction

In contemporary neuroscience and locomotor research, objective gait quantification is now imperative to dissecting neural-muscular functions, elucidating disease mechanisms, and evaluating rehabilitation strategies¹. Its high spatiotemporal resolution enables capture of subtle variations in synergies among neural commands, muscle activation profiles, and skeletal mechanics, thus establishing an empirical foundation for investigating the coordination of supraspinal pathways, peripheral feedback, and mechanical contexts. Reproducible kinematic metrics enhance detection sensitivity for lesions, degenerative changes, and therapeutic effects, accele....

Protocol

All animal procedures were approved by the Institutional Animal Care and Use Committee of the Beijing Institute for Brain Research and complied with the Guidelines for the Care and Use of Laboratory Animals. No procedure-related adverse events were observed.

1. Equipment preparation

1. Turn on the power supply of the treadmill system and select the Speed Control button on the touchscreen.
2. Let the belt run at a low speed for 30 s to observe whether the treadmill belt deviates.
3. Select the Incline Control button, then set the incline to 0°, click Zero, and con....

Results

The schematic diagram and actual photo of the treadmill system structure are shown in Figure 1A,B, respectively. The device is capable of obtaining high-contrast lateral images without markers. The treadmill supports adjustments for speed, incline, direction, and weight reduction, and is equipped with a numerical control panel. The accompanying software interface includes a real-time monitoring and acquisition interface, as well as a video upload and gait extraction analysis.......

Discussion

This protocol delineates a treadmill-based gait analysis system for rats and its operational procedures, providing a multidimensional and objective assessment tool for neuromuscular behavioral research. As demonstrated in the intergroup comparisons of gait parameters between SCI and healthy rats within the exemplar study, the system acquires raw spatial movement coordinates of subjects' lower limbs through continuous high-frame-rate gait videography and an AI-driven automated multi-joint annotation algorithm. This en.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Gait AI Annotation & Acquisition Software	Shanghai ClinAITrial Technology Co., Ltd.	MouseAI	MouseAI is the integrated analysis software for the MOVEMETRICS treadmill, unifying video recording, metadata logging, and automated gait analysis into a single workflow.
Mechanical impact	RWD Life Science, Shenzhen, China
Multi-channel electrophysio logical recording system	Kedou (suzhou) brain- computer Technology Co. ,	KedouBC RHD128A	KedouBC RHD128A is a compact, 128-channel in vivo electrophysiology acquisition system engineered for high-resolution neural recording in small to large laboratory animals. Integrating lightweight, plug-and-play headstages (≤ 1.3 g), 16-bit A/D conversion and programmable amplifiers, it captures EEG, ECoG, LFP, spikes, EMG and ECG signals from 0.1 Hz to 30 kHz with ≤ 2.4 µV system noise and > 10 GΩ input impedance. RHD128A delivers robust, user- scalable neural data to accelerate studies in brain-machine interfaces, neurodegeneration and rehabilitation.
Treadmill	Shanghai ClinAITrial Technology Co., Ltd.	MOVEMETRICS	MOVEMETRICS is a high-precision instrumented treadmill for advanced rodent gait analysis.