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.

翻译：瑜伽因其在提升体能、柔韧性与心理健康方面的效果而广受认可。然而，这些益处高度依赖于正确的体式执行。在瑜伽练习中，尤其是自学或在线训练环境下，不当的身体对齐会降低练习效果，并增加肌肉骨骼损伤的风险。本文提出了一种基于混合边缘AI的实时瑜伽姿态检测与体式校正框架。该系统集成了轻量级人体姿态估计模型、生物力学特征提取以及基于CNN-LSTM的时间序列学习架构，用于识别瑜伽体式并分析运动动态。通过计算检测到的关键点的关节角度与骨骼特征，并与参考体式配置进行对比，评估姿态的正确性。引入了一种量化评分机制，用于衡量对齐偏差，并通过视觉、文本及语音引导方式生成实时的校正反馈。此外，应用了模型量化和剪枝等边缘AI优化技术，以在资源受限设备上实现低延迟性能。该框架提供了一种智能且可扩展的数字瑜伽助手，能够提升现代健身应用中用户的安全性与训练效果。