Real-time detection of gait phase is a critical challenge for closed-loop neuromodulation systems aimed at restoring locomotion after spinal cord injury (SCI). However, many existing gait analysis approaches rely on offline processing or computationally intensive models that are unsuitable for low-latency, embedded deployment. In this study, we present a hybrid AI-based sensing architecture that enables real-time kinematic extraction and on-device gait-phase classification for closed-loop neuromodulation in SCI mice. A vision AI module performs marker-assisted, high-speed pose estimation to extract hindlimb joint angles during treadmill locomotion, while a lightweight edge AI model deployed on a microcontroller classifies gait phase and generates real-time phase-dependent stimulation triggers for closed-loop neuromodulation. The integrated system generalized to unseen SCI gait patterns without injury-specific retraining and enabled precise phase-locked biphasic stimulation in a bench-top closed-loop evaluation. This work demonstrates a low-latency, attachment-free sensing and control framework for gait-responsive neuromodulation, supporting future translation to wearable or implantable closed-loop neurorehabilitation systems.

Real-time detection of gait phase is a critical challenge for closed-loop neuromodulation systems aimed at restoring locomotion after spinal cord injury (SCI). However, many existing gait analysis approaches rely on offline processing or computationally intensive models that are unsuitable for low-latency, embedded deployment. In this study, we present a hybrid AI-based sensing architecture that enables real-time kinematic extraction and on-device gait phase classification for closed-loop neuromodulation in SCI mice. A vision AI module performs marker-assisted, high-speed pose estimation to extract hindlimb joint angles during treadmill locomotion, while a lightweight edge AI model deployed on a microcontroller classifies gait phase and generates real-time phase-dependent stimulation triggers for closed-loop neuromodulation. The integrated system generalized to unseen SCI gait patterns without injury-specific retraining and enabled precise phase-locked biphasic stimulation in a bench-top closed-loop evaluation. This work demonstrates a low-latency, attachment-free sensing and control framework for gait-responsive neuromodulation, supporting future translation to wearable or implantable closed-loop neurorehabilitation systems.

Closed-loop neuromodulation systems for spinal cord injury (SCI) comprise a sensor layer that detects motor state or intent, a controller that converts this information into stimulation commands, and a stimulation interface that modulates spinal, peripheral, or supraspinal circuits. Although neuromodulation has shown potential for improving stepping, standing, trunk control, and upper-limb function after SCI, the performance of closed-loop systems depends critically on their reliability, latency, and practicality. This structured narrative review focuses on the sensor inputs used in closed-loop neuromodulation for SCI, including kinematic sensors, electromyography, force and pressure sensors, vision-based sensing, and emerging neural interfaces. We summarize how these signals have been used to estimate gait phase, posture, motor intent, and task state, and how sensor-driven strategies have progressed from animal models to early clinical applications. Preclinical studies provide mechanistic insights into activity-dependent, phase-specific, and proprioceptive feedback-mediated control, whereas human studies suggest that sensor-guided stimulation may improve functional specificity; however, comparative evidence remains limited. Future translation will require robust multimodal sensing, standardized reporting of latency and calibration burden, and practical designs suitable for supervised clinical and, ultimately, home use.

Vascular disruption is an early and critical event in spinal cord injury (SCI), but its contribution remains poorly understood. Using novel in vivo two-photon dual-dye imaging, we found simultaneous blood-spinal cord barrier (BSCB) leakage and venous dilation in both the injury epicenter and adjacent transitional segment after cervical SCI in rats. Notably, vascular permeability in the transitional zone preceded axonal and neuronal loss, revealing a therapeutic window. Systemic delivery of ferulic acid-glycol chitosan (FA-GC) nanoparticles, a membrane-sealant, rapidly stabilized the compromised vasculature, reduced neuronal loss in the transitional region, and improved forelimb muscle strength. These findings identified acute vascular leakage beyond the injury epicenter as a driver of secondary pathology and highlight early vascular stabilization as promising therapeutic strategy.