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AI-Based Stroke Rehabilitation Domiciliary Assessment System with ST_GCN Attention

arXiv 2510.00049 ↗First posted 27 Sep 2025eess.IV cs.AI cs.CVcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 51 references that are in the map. First posted on arXiv 27 Sep 2025.

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References · 17

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019Deep High-Resolution Representation Learning for Human Pose Estimationcited by 1,339
  3. 2018Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognitioncited by 1,199
  4. 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,161
  5. 2021Revisiting Skeleton-based Action Recognitioncited by 256
  6. 2019Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognitioncited by 135
  7. 2020Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunitiescited by 125
  8. 2018Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregationcited by 106
  9. 2021Skeleton Aware Multi-modal Sign Language Recognitioncited by 101
  10. 2017Skeleton-Based Human Action Recognition with Global Context-Aware Attention LSTM Networkscited by 62
  11. 2021Attention, please! A survey of Neural Attention Models in Deep Learningcited by 50
  12. 2020Richly Activated Graph Convolutional Network for Robust Skeleton-based Action Recognitioncited by 36
5 more
  1. 2024Unlocking the Power of LSTM for Long Term Time Series Forecastingcited by 29
  2. 2019A Deep Learning Framework for Assessing Physical Rehabilitation Exercisescited by 26
  3. 2022ZoomNAS: Searching for Whole-body Human Pose Estimation in the Wildcited by 23
  4. 2024Expressive Keypoints for Skeleton-based Action Recognition via Skeleton Transformationcited by 6
  5. 2023MR-STGN: Multi-Residual Spatio Temporal Graph Network Using Attention Fusion for Patient Action Assessmentcited by 5

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