基于深度学习的多关节同步跟踪，客观量化大鼠后肢运动运动学

Overview

This article presents a markerless, treadmill-based gait analysis system for rodents that leverages custom deep learning algorithms to enable real-time, multidimensional tracking of lower-limb joint kinematics. The system provides objective, high-throughput quantification of gait parameters under various experimental conditions, and is validated using spinal cord injury (SCI) models to demonstrate its sensitivity and utility in neuromuscular research.

Key Study Components

Area of Science

• Neuroscience
• Kinesiology
• Behavioral analysis
• Biomedical engineering

Background

• Accurate assessment of rodent gait is critical for studying neuromuscular and neurological disorders.
• Traditional footprint-based methods are limited in capturing complex, multi-joint kinematics.
• Objective, high-dimensional gait analysis tools are needed to reduce bias and improve data richness.
• Integration of neural signal recording with behavioral analysis can elucidate central-peripheral control mechanisms.

Purpose of Study

• To develop a markerless, AI-driven treadmill system for real-time, multi-joint gait analysis in rodents.
• To enable objective quantification of gait parameters under variable speed, incline, and weight support.
• To validate the system's sensitivity in distinguishing healthy from spinal cord injured rodents.

Methods Used

• Setup of a programmable treadmill with lateral video capture for markerless joint tracking.
• Application of deep learning algorithms for automatic extraction of joint trajectories and gait parameters.
• Experimental modulation of treadmill speed (0–300 mm/s), incline (±30°), and weight support (0–500 g).
• Integration of electrophysiological recordings (EEG, EMG, spinal cord signals) synchronized with gait video.
• Analysis of gait cycles, joint kinematics, force distribution, and movement smoothness.

Main Results

• The system enables real-time, high-throughput extraction of multi-joint trajectories and gait cycle parameters.
• SCI rats exhibited significant loss of swing, irregular iliac displacement, increased joint angle fluctuations, and reduced range of motion compared to healthy controls.
• Heat maps and trajectory analyses revealed impaired movement continuity and altered force distribution in SCI rats.
• Synchronization with neural recordings allowed visualization of electrophysiological patterns across gait phases.

Conclusions

• The markerless treadmill system provides objective, multidimensional gait analysis, overcoming limitations of traditional methods.
• It is sensitive to neuromuscular deficits and suitable for disease discrimination and grading.
• The platform supports integration with neurophysiological recordings, enabling advanced studies of motor control and neuromodulation.

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