Motion data alignment for real-time guidance in avatar based physical therapy training system

Abstract

In this paper, we propose an Avatar based Virtual Reality user training system that efficiently trains users in performing a variety of activities using a pre-recorded avatar. To evaluate and monitor the user's adherence to the avatar's instructions, the system compares the user's motion data against the avatar's motion data, with the latter established as the ground truth dataset. Unfortunately, human reaction delay may cause the motion sequences between the user and the avatar to be misaligned. Consequently, to enable accurate comparison, we analyze four signal processing time delay estimation methods—an existing method and three proposed methods—to align the motion sequences between the user and pre-recorded avatar, allowing the correct frames to be compared. Our experiments demonstrate that the proposed methods perform better data alignment than the existing method and the fourth method, which employs a novel spatial-temporal segmentation algorithm, has the highest potential to be the optimal delay estimation approach. Further, to provide real-time guidance to the user, we determine a unique tolerance threshold for each activity such that a user accuracy value below the threshold value prompts real-time guidance to correct the user and an accuracy value above the threshold is tolerated. We perform an experiment with the assistance of a physical trainer and use the experimental data to design a histogram-based method using Bayesian decision theory to determine the threshold values.