Sensor Inputs and Closed-Loop Neuromodulation in Spinal Cord Injury: From Animal Models to Clinical Translation

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.