# ExeChecker: Where Did I Go Wrong?

arXiv: https://arxiv.org/abs/2412.10573 · HTML: https://mlsanity.com/paper/2412.10573/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 13 Dec 2024.

## Builds on (most cited first, 12 of 12)

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Learning Fine-grained Image Similarity with Deep Ranking](https://mlsanity.com/paper/1404.4661/) (2014)
- [What and How Well You Performed? A Multitask Learning Approach to Action Quality Assessment](https://mlsanity.com/paper/1904.04346/) (2019)
- [PoseFormerV2: Exploring Frequency Domain for Efficient and Robust 3D Human Pose Estimation](https://mlsanity.com/paper/2303.17472/) (2023)
- [Learning To Score Olympic Events](https://mlsanity.com/paper/1611.05125/) (2016)
- [Uncertainty-aware Score Distribution Learning for Action Quality Assessment](https://mlsanity.com/paper/2006.07665/) (2020)
- [A Deep Learning Framework for Assessing Physical Rehabilitation Exercises](https://mlsanity.com/paper/1901.10435/) (2019)
- [Domain Knowledge-Informed Self-Supervised Representations for Workout Form Assessment](https://mlsanity.com/paper/2202.14019/) (2022)
- [D-STGCNT: A Dense Spatio-Temporal Graph Conv-GRU Network based on transformer for assessment of patient physical rehabilitation](https://mlsanity.com/paper/2401.06150/) (2023)
- [VI-Net: View-Invariant Quality of Human Movement Assessment](https://mlsanity.com/paper/2008.04999/) (2020)

## Abstract (arXiv)

In this paper, we present a contrastive learning based framework, ExeChecker, for the interpretation of rehabilitation exercises. Our work builds upon state-of-the-art advances in the area of human pose estimation, graph-attention neural networks, and transformer interpretablity. The downstream task is to assist rehabilitation by providing informative feedback to users while they are performing prescribed exercises. We utilize a contrastive learning strategy during training. Given a tuple of correctly and incorrectly executed exercises, our model is able to identify and highlight those joints that are involved in an incorrect movement and thus require the user's attention. We collected an in-house dataset, ExeCheck, with paired recordings of both correct and incorrect execution of exercises. In our experiments, we tested our method on this dataset as well as the UI-PRMD dataset and found ExeCheck outperformed the baseline method using pairwise sequence alignment in identifying joints of physical relevance in rehabilitation exercises.