Human Posture Detection in Virtual Reality Applications for Stress Reduction

Abstract

This research explores the potential of human pose estimation (HPE) using machine learning to analyze user movements within a virtual reality (VR) environment for stress assessment. By analyzing 2D video footage captured during VR sessions, HPE provides a computationally efficient method for tracking user poses, making it an ideal tool for evaluating therapeutic exercises. However, challenges such as clothing occlusions, background complexity, and varying lighting conditions can affect HPE accuracy. This study investigates the effectiveness of HPE in overcoming these challenges within a VR therapy context. We propose a robust system designed to accurately assess user posture during VR therapy simulations. Our approach leverages advanced machine learning algorithms to enhance the precision of pose estimation, even under challenging conditions. Preliminary results indicate that our system achieves an impressive overall accuracy of 94%, demonstrating its potential to provide reliable assessments of user movements.