# Recognizing Exercises and Counting Repetitions in Real Time

arXiv: https://arxiv.org/abs/2005.03194 · HTML: https://mlsanity.com/paper/2005.03194/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2020.

## Built on by (most cited first, 5 of 5)

- [InfiniteForm: A synthetic, minimal bias dataset for fitness applications](https://mlsanity.com/paper/2110.01330/) (2021)
- [Using Learnable Physics for Real-Time Exercise Form Recommendations](https://mlsanity.com/paper/2310.07221/) (2023)
- [A HRNet-based Rehabilitation Monitoring System](https://mlsanity.com/paper/2306.10756/) (2023)
- [P=uioio: On-device Real-Time Smartphone-Based Automated Exercise Repetition Counting System](https://mlsanity.com/paper/2308.02420/) (2023)
- [Real-Time Fitness Exercise Classification and Counting from Video Frames](https://mlsanity.com/paper/2411.11548/) (2024)

## 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.