MR-STGN: Multi-Residual Spatio Temporal Graph Network Using Attention Fusion for Patient Action Assessment

• URL: http://arxiv.org/abs/2312.13509v1
• Date: Thu, 21 Dec 2023 01:09:52 GMT
• Title: MR-STGN: Multi-Residual Spatio Temporal Graph Network Using Attention Fusion for Patient Action Assessment
• Authors: Youssef Mourchid, Rim Slama
• Abstract summary: We propose an automated approach for patient action assessment using a Multi-Residual Spatio Temporal Graph Network (MR-STGN) The MR-STGN is specifically designed to capture the dynamics of patient actions. We evaluate our model on the UI-PRMD dataset demonstrating its performance in accurately predicting real-time patient action scores.
• Score: 0.3626013617212666
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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