The integration of virtual reality (VR) systems and motion tracking represents a transformative opportunity for both immersive simulations and physical therapy, yet a disconnect remains between real-life movement and VR experiences. This gap poses challenges in both entertainment applications and therapeutic practices, particularly in gait rehabilitation, where smooth and naturalistic motion feedback is critical. Without accurate gait speed data derived from frame-by-frame positional analysis and validated thresholds, avatar movement becomes jerky, inducing motion sickness and reducing the effectiveness of therapeutic exercises. This work focuses on improving the user experience by reducing motion sickness and enhancing immersive walking in VR on a self-paced treadmill. To achieve this goal, we developed a detailed understanding of how our program collects data from VR and wearable motion sensors, ensuring smooth transitions between real-life walking and treadmill use. We designed an algorithm that simulates real-time foot movement based on frame-by-frame positional data and personalized gait elevation metrics, comparing these measurements against validated thresholds to reduce inconsistencies in motion sensor data. The measurements drive avatar and camera movement, synchronizing with built-in VR headset and motion tracking mechanics. Preliminary tests, conducted using Mocopi motion tracking systems with Unreal Engine (UE) VR, indicate improved avatar movement accuracy and reduced motion sickness compared to default systems. The algorithm, now integrated with obstacle avoidance, has paved the way for an ongoing user study to further evaluate its impact on user-avatar synchronization and immersive walking experiences in VR. Future work includes refining and expanding motion algorithms and conducting user studies with physical therapy participants to evaluate the system’s effectiveness in gait training and rehabilitation scenarios.