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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

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.

Keywords


Mediapipe; OpenCV; Rehabilitation training; Unmanned movement teaching

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References


[1] Wang Rubin, DOU Quanli, Zhang Qi, ZHOU Cheng. Gesture Recognition based on MediaPipe for Remote Operation Control of Excavator [J]. Information technology in civil engineering and construction: 1-8 [2022-04-23]. HTTP: / / http://kns.cnki.net/kcms/detail/11.5823.TU.20211125.1918.024.html

[2] Mei Zaixia, Yin Chun, ZHANG Lei. 6G Vision, Application Scenarios and key technologies analysis [C]//.Push the evolution of Promote the application of innovation - 5 g network conference (2021), vol.,

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




DOI: http://dx.doi.org/10.18686/ahe.v6i15.5196

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