Computer Vision-Based Human Body Posture Correction System

With the development of technology and the progress of life, more and more people, regardless of entertainment, learning, or work, cannot do without computer desks and cannot put down their mobile phones. Due to prolonged sitting and often neglecting the importance of posture, incorrect posture can often lead to health problems such as hunchback, lumbar muscle strain, and shoulder and neck pain over time. To address this issue, we designed a computer vision-based human body posture detection system. The system utilizes YOLOv8 technology to accurately locate key points of the human body skeleton, and then analyzes the coordinate positions and depth information of these key points to establish a criterion for distinguishing different postures. With the assistance of an SVM classifier, the system achieves an average recognition rate of 95%. Finally, we successfully deployed the posture detection system on Raspberry Pi hardware and conducted extensive testing. The test results demonstrate that the system can effectively detect various postures and provide real-time reminders to users to correct poor posture, demonstrating good practicality and stability.