An Intelligent Framework for Real-Time Yoga Pose Detection and Posture Correction

TL;DR

This paper introduces a hybrid Edge AI framework for real-time yoga pose detection and correction, combining lightweight models, biomechanical analysis, and feedback mechanisms to enhance safety and effectiveness.

Contribution

It presents a novel integrated system that uses biomechanical features and Edge AI optimization for accurate, low-latency yoga posture assessment and guidance.

Findings

Achieves real-time performance on resource-constrained devices.

Provides multi-modal corrective feedback including visual, text, and voice.

Demonstrates improved posture accuracy and user safety in yoga practice.

Abstract

Yoga is widely recognized for improving physical fitness, flexibility, and mental well being. However, these benefits depend strongly on correct posture execution. Improper alignment during yoga practice can reduce effectiveness and increase the risk of musculoskeletal injuries, especially in self guided or online training environments. This paper presents a hybrid Edge AI based framework for real time yoga pose detection and posture correction. The proposed system integrates lightweight human pose estimation models with biomechanical feature extraction and a CNN LSTM based temporal learning architecture to recognize yoga poses and analyze motion dynamics. Joint angles and skeletal features are computed from detected keypoints and compared with reference pose configurations to evaluate posture correctness. A quantitative scoring mechanism is introduced to measure alignment deviations and generate real time corrective feedback through visual, text based, and voice based guidance. In addition, Edge AI optimization techniques such as model quantization and pruning are applied to enable low latency performance on resource constrained devices. The proposed framework provides an intelligent and scalable digital yoga assistant that can improve user safety and training effectiveness in modern fitness applications.