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.
Recognizing Exercises and Counting Repetitions in Real Time
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abstract
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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cs.CV 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
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Real-Time Fitness Exercise Classification and Counting from Video Frames
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.