Recognizing Exercises and Counting Repetitions in Real Time
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 13 Oct 2026). First posted on arXiv 7 May 2020.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 3; 2024: 1.
5 papers.
- 2021InfiniteForm: A synthetic, minimal bias dataset for fitness applicationscited by 5
- 2023Using Learnable Physics for Real-Time Exercise Form Recommendationscited by 4
- 2023A HRNet-based Rehabilitation Monitoring Systemcited by 0
- 2023P=uioio: On-device Real-Time Smartphone-Based Automated Exercise Repetition Counting Systemcited by 0
- 2024Real-Time Fitness Exercise Classification and Counting from Video Framescited by 0
- 2024Real-Time Fitness Exercise Classification and Counting from Video Frames18 Nov 2024
- 2023Using Learnable Physics for Real-Time Exercise Form Recommendations11 Oct 2023
- 2023P=uioio: On-device Real-Time Smartphone-Based Automated Exercise Repetition Counting System22 Jul 2023
- 2023A HRNet-based Rehabilitation Monitoring System19 Jun 2023
- 2021InfiniteForm: A synthetic, minimal bias dataset for fitness applications4 Oct 2021