# AI-Based Stroke Rehabilitation Domiciliary Assessment System with ST_GCN Attention

arXiv: https://arxiv.org/abs/2510.00049 · HTML: https://mlsanity.com/paper/2510.00049/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 51 references that are in the map.
- First posted on arXiv 27 Sep 2025.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Revisiting Skeleton-based Action Recognition](https://mlsanity.com/paper/2104.13586/) (2021)
- [Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition](https://mlsanity.com/paper/1904.01189/) (2019)
- [Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities](https://mlsanity.com/paper/2001.07416/) (2020)
- [Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation](https://mlsanity.com/paper/1804.06055/) (2018)
- [Skeleton Aware Multi-modal Sign Language Recognition](https://mlsanity.com/paper/2103.08833/) (2021)
- [Skeleton-Based Human Action Recognition with Global Context-Aware Attention LSTM Networks](https://mlsanity.com/paper/1707.05740/) (2017)
- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [Richly Activated Graph Convolutional Network for Robust Skeleton-based Action Recognition](https://mlsanity.com/paper/2008.03791/) (2020)
- [Unlocking the Power of LSTM for Long Term Time Series Forecasting](https://mlsanity.com/paper/2408.10006/) (2024)
- [A Deep Learning Framework for Assessing Physical Rehabilitation Exercises](https://mlsanity.com/paper/1901.10435/) (2019)
- [ZoomNAS: Searching for Whole-body Human Pose Estimation in the Wild](https://mlsanity.com/paper/2208.11547/) (2022)
- [Expressive Keypoints for Skeleton-based Action Recognition via Skeleton Transformation](https://mlsanity.com/paper/2406.18011/) (2024)
- [MR-STGN: Multi-Residual Spatio Temporal Graph Network Using Attention Fusion for Patient Action Assessment](https://mlsanity.com/paper/2312.13509/) (2023)

## Abstract (arXiv)

Effective stroke recovery requires continuous rehabilitation integrated with daily living. To support this need, we propose a home-based rehabilitation exercise and feedback system. The system consists of (1) hardware setup with RGB-D camera and wearable sensors to capture stroke movements, (2) a mobile application for exercise guidance, and (3) an AI server for assessment and feedback. When a stroke user exercises following the application guidance, the system records skeleton sequences, which are then assessed by the deep learning model, RAST-G@ (Rehabilitation Assessment Spatio-Temporal Graph ATtention). The model employs a spatio-temporal graph convolutional network to extract skeletal features and integrates transformer-based temporal attention to figure out action quality. For system implementation, we constructed the NRC dataset, include 10 upper-limb activities of daily living (ADL) and 5 range-of-motion (ROM) collected from stroke and non-disabled participants, with Score annotations provided by licensed physiotherapists. Results on the KIMORE and NRC datasets show that RAST-G@ improves over baseline in terms of MAD, RMSE, and MAPE. Furthermore, the system provides user feedback that combines patient-centered assessment and monitoring. The results demonstrate that the proposed system offers a scalable approach for quantitative and consistent domiciliary rehabilitation assessment.