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Recognizing Exercises and Counting Repetitions in Real Time

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arxiv 2005.03194 v1 pith:Y4MRSP4H submitted 2020-05-07 cs.CV

Talal Alatiah , Chen Chen This is my paper · ORCID
classification cs.CV
keywords repetitionsestimationexercisesfitnessindustrylearningmachinepose
verification ladder T0 review T1 audit T2 compute T3 formal

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Artificial intelligence technology has made its way absolutely necessary in a variety of industries including the fitness industry. Human pose estimation is one of the important researches in the field of Computer Vision for the last few years. In this project, pose estimation and deep machine learning techniques are combined to analyze the performance and report feedback on the repetitions of performed exercises in real-time. Involving machine learning technology in the fitness industry could help the judges to count repetitions of any exercise during Weightlifting or CrossFit competitions.

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  1. Real-Time Fitness Exercise Classification and Counting from Video Frames

    cs.CV 2024-11 conditional novelty 5.0 of 10

    A BiLSTM using 30-frame windows of joint angles plus raw coordinates reports 99% test accuracy on four exercises, with 95% and 88% accuracy on two additional real-world test sets.

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