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

  • Ling Li Lingnan Normal University School of Electronic and Electrical Engineering
  • Huijuan Huang Lingnan Normal University School of Electronic and Electrical Engineering
  • Shaogeng Zeng Guandong Provincial Key Laboratory of Development and Education for Special Needs Children;Lingnan Normal University School of Computer and Intelligent Education;Guangdong University Digital Learning Engineering Technology Development Center
  • Huiqi Cao Lingnan Normal University School of Electronic and Electrical Engineering
  • Rongrui Zheng Lingnan Normal University School of Electronic and Electrical Engineering
  • Shuimei Lin Lingnan Normal University School of Business
DOI: https://doi.org/10.18282/l-e.v10i8.3115
Keywords: Mediapipe, OpenCV, Rehabilitation training, Unmanned movement teaching

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.

References

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2021:387-390. The DOI: 10.26914 / Arthur c. nkihy. 2021.039175.

Published
2022-06-20
Issue
Vol. 10 No. 8 (2021)
Section
Articles

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