# D-STGCNT: A Dense Spatio-Temporal Graph Conv-GRU Network based on transformer for assessment of patient physical rehabilitation

arXiv: https://arxiv.org/abs/2401.06150 · HTML: https://mlsanity.com/paper/2401.06150/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 63 references that are in the map.
- First posted on arXiv 21 Dec 2023.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [3D human pose estimation in video with temporal convolutions and semi-supervised training](https://mlsanity.com/paper/1811.11742/) (2018)
- [Actional-Structural Graph Convolutional Networks for Skeleton-based Action Recognition](https://mlsanity.com/paper/1904.12659/) (2019)
- [Human Action Recognition from Various Data Modalities: A Review](https://mlsanity.com/paper/2012.11866/) (2020)
- [Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition](https://mlsanity.com/paper/1904.01189/) (2019)
- [Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation](https://mlsanity.com/paper/1804.06055/) (2018)
- [Skeleton-based Action Recognition via Spatial and Temporal Transformer Networks](https://mlsanity.com/paper/2008.07404/) (2020)
- [Richly Activated Graph Convolutional Network for Robust Skeleton-based Action Recognition](https://mlsanity.com/paper/2008.03791/) (2020)
- [A Deep Learning Framework for Assessing Physical Rehabilitation Exercises](https://mlsanity.com/paper/1901.10435/) (2019)
- [Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2011.13322/) (2020)
- [A Review of Computational Approaches for Evaluation of Rehabilitation Exercises](https://mlsanity.com/paper/2003.08767/) (2020)
- [Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1805.07694/) (2018)
- [Movienet: A Movie Multilayer Network Model using Visual and Textual Semantic Cues](https://mlsanity.com/paper/1910.09368/) (2019)
- [Parkinson's Disease Assessment from a Wrist-Worn Wearable Sensor in Free-Living Conditions: Deep Ensemble Learning and Visualization](https://mlsanity.com/paper/1808.02870/) (2018)

## Built on by (most cited first, 9 of 9)

- [A Decade of Action Quality Assessment: Largest Systematic Survey of Trends, Challenges, and Future Directions](https://mlsanity.com/paper/2502.02817/) (2025)
- [An Improved 3D Skeletons UP-Fall Dataset: Enhancing Data Quality for Efficient Impact Fall Detection](https://mlsanity.com/paper/2502.19048/) (2025)
- [Rehabilitation Exercise Quality Assessment through Supervised Contrastive Learning with Hard and Soft Negatives](https://mlsanity.com/paper/2403.02772/) (2024)
- [Skeleton-Based Transformer for Classification of Errors and Better Feedback in Low Back Pain Physical Rehabilitation Exercises](https://mlsanity.com/paper/2504.13866/) (2025)
- [Evaluation Framework for Feedback Generation Methods in Skeletal Movement Assessment](https://mlsanity.com/paper/2404.09359/) (2024)
- [ExeChecker: Where Did I Go Wrong?](https://mlsanity.com/paper/2412.10573/) (2024)
- [Improving Pain Classification using Spatio-Temporal Deep Learning Approaches with Facial Expressions](https://mlsanity.com/paper/2501.06787/) (2025)
- [A Point Cloud Transformer for Remote Monitoring and Automated Assessment of Physical Rehabilitation Exercises](https://mlsanity.com/paper/2606.30309/) (2026)
- [Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data](https://mlsanity.com/paper/2607.25710/) (2026)

## Abstract (arXiv)

This paper tackles the challenge of automatically assessing physical rehabilitation exercises for patients who perform the exercises without clinician supervision. The objective is to provide a quality score to ensure correct performance and achieve desired results. To achieve this goal, a new graph-based model, the Dense Spatio-Temporal Graph Conv-GRU Network with Transformer, is introduced. This model combines a modified version of STGCN and transformer architectures for efficient handling of spatio-temporal data. The key idea is to consider skeleton data respecting its non-linear structure as a graph and detecting joints playing the main role in each rehabilitation exercise. Dense connections and GRU mechanisms are used to rapidly process large 3D skeleton inputs and effectively model temporal dynamics. The transformer encoder's attention mechanism focuses on relevant parts of the input sequence, making it useful for evaluating rehabilitation exercises. The evaluation of our proposed approach on the KIMORE and UI-PRMD datasets highlighted its potential, surpassing state-of-the-art methods in terms of accuracy and computational time. This resulted in faster and more accurate learning and assessment of rehabilitation exercises. Additionally, our model provides valuable feedback through qualitative illustrations, effectively highlighting the significance of joints in specific exercises.