# Assessing Performance of Aerobic Routines using Background Subtraction and Intersected Image Region

arXiv: https://arxiv.org/abs/1810.01564 · HTML: https://mlsanity.com/paper/1810.01564/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2018.

## Built on by (most cited first, 1 of 1)

- [Image-based Agarwood Resinous Area Segmentation using Deep Learning](https://mlsanity.com/paper/2404.05129/) (2024)

## Abstract (arXiv)

It is recommended for a novice to engage a trained and experience person, i.e., a coach before starting an unfamiliar aerobic or weight routine. The coach's task is to provide real-time feedbacks to ensure that the routine is performed in a correct manner. This greatly reduces the risk of injury and maximise physical gains. We present a simple image similarity measure based on intersected image region to assess a subject's performance of an aerobic routine. The method is implemented inside an Augmented Reality (AR) desktop app that employs a single RGB camera to capture still images of the subject as he or she progresses through the routine. The background-subtracted body pose image is compared against the exemplar body pose image (i.e., AR template) at specific intervals. Based on a limited dataset, our pose matching function is reported to have an accuracy of 93.67%.