A Vision-Based Photoanthropometric Approach for Yoga Pose Analysis Using LabVIEW-ML

In this work, we present a novel framework of integrating anthropometric measurement with posture recognition using LabVIEW’s Vision Development Module specifically for yoga poses. Yoga is a practice that originated in India many years ago and is now popular globally, contributing significantly towards balancing the human body and mind. A key challenge is the accurate recognition of yoga poses in real time using contactless techniques. To achieve this, the proposed system works by digitizing yoga poses through anthropometric measurements enhanced through photoanthropometric modeling with Calibrated Bounding Boxes using YOLO version 4 for representing the poses, combined with geometric examinations to normalize bounding box dimensions and proceed for pose variations. The intermediate model developed improves the measurement accuracy, while the final stage handles posture classification in the LabVIEW ML environment for real-time yoga pose recognition. This study focuses on the Tadasana pose. A Microsoft XBOX 360 Kinect sensor was used for capturing the images, which were processed using LabVIEW 2019 Vision IMAQ. An inference model was developed for feature extraction and matching, such that precise posture identification was facilitated with bounding boxes. The experimental results illustrate that the proposed system achieves a pose recognition accuracy, with respect to anthropometric measurements, of 95.4%, thus proving its potential for rehabilitation and health monitoring.