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MR-STGN: Multi-Residual Spatio Temporal Graph Network Using Attention Fusion for Patient Action Assessment
Computer Vision and Pattern Recognition · Submitted 21 Dec 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We evaluate our model on the UI-PRMD dataset demonstrating its performance in accurately predicting real-time patient action scores, surpassing state-of-the-art methods.
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The abstract, sentence by sentence
Accurate assessment of patient actions plays a crucial role in healthcare as it contributes significantly to disease progression monitoring and treatment effectiveness. However, traditional approaches to assess patient actions often rely on manual observation and scoring, which are subjective and time-consuming. In this paper, we propose an automated approach for patient action assessment using a Multi-Residual Spatio Temporal Graph Network (MR-STGN) that incorporates both angular and positional 3D skeletons. The MR-STGN is specifically designed to capture the spatio-temporal dynamics of patient actions. It achieves this by integrating information from multiple residual layers, with each layer extracting features at distinct levels of abstraction. Furthermore, we integrate an attention fusion mechanism into the network, which facilitates the adaptive weighting of various features. This empowers the model to concentrate on the most pertinent aspects of the patient's movements, offering precise instructions regarding specific body parts or movements that require attention. Ablation studies are conducted to analyze the impact of individual components within the proposed model. We evaluate our model on the UI-PRMD dataset demonstrating its performance in accurately predicting real-time patient action scores, surpassing state-of-the-art methods.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Accurate assessment of patient actions plays a crucial role in healthcare as it contributes significantly to disease progression monitoring and treatment effectiveness. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | However, traditional approaches to assess patient actions often rely on manual observation and scoring, which are subjective and time-consuming. | gap 0.51 | 0.05 | 0.02 | asserted 0.98 |
| s2 | In this paper, we propose an automated approach for patient action assessment using a Multi-Residual Spatio Temporal Graph Network (MR-STGN) that incorporates both angular and positional 3D skeletons. | contribution 0.88 | 0.85 | 0.11 | asserted 0.99 |
| s3 | The MR-STGN is specifically designed to capture the spatio-temporal dynamics of patient actions. | method 0.86 | 0.51 | 0.04 | asserted 1.00 |
| s4 | It achieves this by integrating information from multiple residual layers, with each layer extracting features at distinct levels of abstraction. | method 0.99 | 0.70 | 0.03 | asserted 1.00 |
| s5 | Furthermore, we integrate an attention fusion mechanism into the network, which facilitates the adaptive weighting of various features. | method 0.92 | 0.93 | 0.04 | asserted 1.00 |
| s6 | This empowers the model to concentrate on the most pertinent aspects of the patient's movements, offering precise instructions regarding specific body parts or movements that require attention. | method 0.58 | 0.55 | 0.08 | asserted 0.96 |
| s7 | Ablation studies are conducted to analyze the impact of individual components within the proposed model. | method 0.93 | 0.94 | 0.02 | asserted 0.97 |
| s8 | We evaluate our model on the UI-PRMD dataset demonstrating its performance in accurately predicting real-time patient action scores, surpassing state-of-the-art methods. | result 0.99 | 0.97 | 0.76 | asserted 1.00 |
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Original abstract
Accurate assessment of patient actions plays a crucial role in healthcare as it contributes significantly to disease progression monitoring and treatment effectiveness. However, traditional approaches to assess patient actions often rely on manual observation and scoring, which are subjective and time-consuming. In this paper, we propose an automated approach for patient action assessment using a Multi-Residual Spatio Temporal Graph Network (MR-STGN) that incorporates both angular and positional 3D skeletons. The MR-STGN is specifically designed to capture the spatio-temporal dynamics of patient actions. It achieves this by integrating information from multiple residual layers, with each layer extracting features at distinct levels of abstraction. Furthermore, we integrate an attention fusion mechanism into the network, which facilitates the adaptive weighting of various features. This empowers the model to concentrate on the most pertinent aspects of the patient's movements, offering precise instructions regarding specific body parts or movements that require attention. Ablation studies are conducted to analyze the impact of individual components within the proposed model. We evaluate our model on the UI-PRMD dataset demonstrating its performance in accurately predicting real-time patient action scores, surpassing state-of-the-art methods.
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Additionally, we include the co-occurrence model [ref] , PA-LSTM [ref] , two-stream CNN [ref] , and hierarchical LSTM [ref] .
Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation · Experimental Results · 0.97
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