Background: Stroke often leads to persistent gait impairments that significantly reduce mobility and quality of life. Conventional rehabilitation has demonstrated therapeutic value but is limited by insufficient personalization and low patient engagement.
Objectives: This study aimed to evaluate the clinical effectiveness of a realtime Kinect-based motion analysis and AI-driven virtual reality (VR) gait training system for stroke rehabilitation.
Design: Randomized controlled trial with parallel-group assignment.
Methods: Thirty stroke patients were randomly assigned to a VR-based gait training group (n=15) or a conventional physical therapy group (n=15) for 8 weeks. The VR system integrated Kinect-based markerless motion capture, a 14-layer artificial neural network for gait parameter prediction, and immersive VR feedback to provide personalized gait retraining. Spatiotemporal gait parameters—including gait velocity, step length, cadence, and step width— were assessed before and after the intervention.
Results: The VR group demonstrated significantly greater improvements in gait velocity (0.52 to 0.73 m/s, +40.4%), step length (78.3 to 95.7 cm, +22.2%), and cadence (100.2 to 110.4 steps/min, +10.2%) than the control group, while step width decreased (12.3 to 9.8 cm, −20.3%), indicating enhanced balance and stability. The artificial neural network accurately predicted movement patterns and supported adaptive training with real-time feedback.
Conclusion: The real-time VR gait rehabilitation system effectively enhanced gait performance and motor coordination among stroke patients, outperforming conventional physical therapy. The integration of Kinect-based motion capture and AI-driven personalization provides a promising platform for scalable and clinically meaningful stroke rehabilitation.