用轻量AI实时检测瑜伽姿势并纠正，提升自练安全性
An Intelligent Framework for Real-Time Yoga Pose Detection and Posture Correction
- 融合姿态估计与生物力学分析，动态识别动作
- 通过关节角度比对实现姿势偏差量化评分
- 支持低延迟反馈，适合手机等设备部署
瑜伽广受认可，有助于提升体能、柔韧性和心理健康，但其效果高度依赖正确姿势。错误的体态会降低训练效果并增加运动损伤风险，尤其在自主或在线训练中更为突出。本文提出一种基于边缘AI的混合框架，实现实时瑜伽姿势检测与姿态纠正。系统结合轻量级人体姿态估计算法、生物力学特征提取，以及基于CNN-LSTM的时序学习架构，识别瑜伽姿势并分析动作动态。从关键点计算关节角度与骨骼特征，并与标准姿势进行对比，评估姿势正确性。引入量化评分机制，测量对齐偏差，并通过视觉、文本和语音方式提供实时纠正反馈。同时采用模型量化和剪枝等边缘AI优化技术，确保在资源受限设备上实现低延迟运行。该框架可作为智能、可扩展的数字瑜伽助手，有效提升现代健身应用中的用户安全与训练效果。
原文摘要 · Abstract (English)
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.
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