# Viewpoint-Invariant Exercise Repetition Counting

arXiv: https://arxiv.org/abs/2107.13760 · HTML: https://mlsanity.com/paper/2107.13760/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 38 references that are in the map.
- First posted on arXiv 29 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Segmentation Transformer: Object-Contextual Representations for Semantic Segmentation](https://mlsanity.com/paper/1909.11065/) (2019)
- [A simple yet effective baseline for 3d human pose estimation](https://mlsanity.com/paper/1705.03098/) (2017)
- [Counting Out Time: Class Agnostic Video Repetition Counting in the Wild](https://mlsanity.com/paper/2006.15418/) (2020)
- [Real-World Repetition Estimation by Div, Grad and Curl](https://mlsanity.com/paper/1802.09971/) (2018)

## Built on by (most cited first, 3 of 3)

- [Rehabilitation Exercise Repetition Segmentation and Counting using Skeletal Body Joints](https://mlsanity.com/paper/2304.09735/) (2023)
- [Count What You Want: Exemplar Identification and Few-shot Counting of Human Actions in the Wild](https://mlsanity.com/paper/2312.17330/) (2023)
- [P=uioio: On-device Real-Time Smartphone-Based Automated Exercise Repetition Counting System](https://mlsanity.com/paper/2308.02420/) (2023)

## Abstract (arXiv)

Counting the repetition of human exercise and physical rehabilitation is a common task in rehabilitation and exercise training. The existing vision-based repetition counting methods less emphasize the concurrent motions in the same video. This work presents a vision-based human motion repetition counting applicable to counting concurrent motions through the skeleton location extracted from various pose estimation methods. The presented method was validated on the University of Idaho Physical Rehabilitation Movements Data Set (UI-PRMD), and MM-fit dataset. The overall mean absolute error (MAE) for mm-fit was 0.06 with off-by-one Accuracy (OBOA) 0.94. Overall MAE for UI-PRMD dataset was 0.06 with OBOA 0.95. We have also tested the performance in a variety of camera locations and concurrent motions with conveniently collected video with overall MAE 0.06 and OBOA 0.88. The proposed method provides a view-angle and motion agnostic concurrent motion counting. This method can potentially use in large-scale remote rehabilitation and exercise training with only one camera.