Edge AI-Based Gait-Phase Detection for Closed-Loop Neuromodulation in SCI Mice

TL;DR

This paper introduces a real-time AI system for detecting gait phases in SCI mice to enable closed-loop neuromodulation for restoring locomotion.

Contribution

A hybrid edge AI architecture for real-time, attachment-free gait-phase detection and stimulation in SCI mice.

Findings

The system generalized to unseen SCI gait patterns without injury-specific retraining.

Precise phase-locked biphasic stimulation was achieved in a bench-top closed-loop evaluation.

The framework supports future wearable or implantable neurorehabilitation systems.

Abstract

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.

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