AI Body Detection and Teaching System based on Mediapipe Machine Learning Platform and OpenCV Computer Vision Library

Ling Li, Huijuan Huang, Shaogeng Zeng, Huiqi Cao, Rongrui Zheng, Shuimei Lin
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Abstract

To solve the problems of low intera ctivity, high cost, large amount of data, “difficult to quantify, difficult to record, difficult to supervise, difficult to analyze” of human motion detection correction devices on the market today, we designed an intelligent device based on Mediapipe machine learning platform and OpenCV computer based on Raspberry Pi, camera and display. We designed an intelligent device for AI body detection and teaching based on Mediapipe machine learning platform and OpenCV computer vision library. By combining chip, sensor, computing platform and technology level of computer vision, speech recognition and machine learning, the device can capture human movement in real time by using camera equipment, judge the accuracy and completeness of user’s movement according to the comparison of standard movement, and give feedback to the user in real time by voice broadcast and image prompt. The test results show that the device has the advantages of low cost, simple structure, intelligence, unmanned, data and accuracy, which provides a feasible solution to further enhance the convenience and accuracy of unmanned movement teaching and rehabilitation training.
基于Mediapipe机器学习平台和OpenCV计算机视觉库的AI人体检测与教学系统
针对目前市场上人体运动检测校正装置交互性低、成本高、数据量大、“难以量化、难以记录、难以监督、难以分析”等问题,我们设计了一款基于Mediapipe机器学习平台和基于树莓派、摄像头、显示屏的OpenCV计算机的智能设备。我们基于Mediapipe机器学习平台和OpenCV计算机视觉库,设计了一种用于人工智能身体检测和教学的智能设备。该设备结合芯片、传感器、计算平台和计算机视觉、语音识别、机器学习等技术水平,利用摄像设备实时捕捉人体运动,通过与标准动作的对比判断用户动作的准确性和完整性,并通过语音广播、图像提示等方式实时向用户反馈。试验结果表明,该装置具有成本低、结构简单、智能化、无人化、数据化和准确性等优点,为进一步提高无人运动教学和康复训练的便利性和准确性提供了可行的解决方案。
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