<?xml version="1.0" encoding="UTF-8"?> <article key="pdf/10012992" mdate="2023-03-07 00:00:00"> <author>Zixuan Xu and Yichun Lou and Yang Song and Zihuai Lin</author> <title>Fitness Action Recognition Based on MediaPipe</title> <pages>201 - 208</pages> <year>2023</year> <volume>17</volume> <number>3</number> <journal>International Journal of Electronics and Communication Engineering</journal> <ee>https://publications.waset.org/pdf/10012992</ee> <url>https://publications.waset.org/vol/195</url> <publisher>World Academy of Science, Engineering and Technology</publisher> <abstract>MediaPipe is an opensource machine learning computer vision framework that can be ported into a multiplatform environment, which makes it easier to use it to recognize human activity. Based on this framework, many human recognition systems have been created, but the fundamental issue is the recognition of human behavior and posture. In this paper, two methods are proposed to recognize human gestures based on MediaPipe, the first one uses the Adaptive Boosting algorithm to recognize a series of fitness gestures, and the second one uses the Fast Dynamic Time Warping algorithm to recognize 413 continuous fitness actions. These two methods are also applicable to any human posture movement recognition.</abstract> <index>Open Science Index 195, 2023</index> </article>