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.
Abstract (translated)
瑜伽被广泛认可能够提升体能、柔韧性和心理健康。然而，这些益处很大程度上取决于正确的姿势执行。在瑜伽练习中，若身体排列不当，不仅会降低练习效果，还会增加肌肉骨骼损伤的风险，尤其在自主练习或在线训练环境中更为明显。本文提出一种基于混合边缘AI的实时瑜伽姿势检测与矫正框架。该系统融合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的时序学习架构，用于识别瑜伽姿势并分析动作动态。通过从检测到的关键点计算关节角度与骨骼特征，并与标准姿势配置进行对比，从而评估姿势正确性。研究引入定量评分机制，通过测量对齐偏差，并借助视觉、文本及语音引导生成实时矫正反馈。此外，系统应用模型量化与剪枝等边缘AI优化技术，实现在资源受限设备上的低延迟运行。该框架可作为智能且可扩展的数字瑜伽助手，提升现代健身应用中的用户安全与训练效果。
URL
https://arxiv.org/abs/2603.26760
