A Deep Learning Framework for Assessing Physical Rehabilitation. . Despite the essential role of rehabilitation assessment toward improved patient outcomes and reduced healthcare costs, existing approaches lack versatility, robustness, and practical relevance. In this paper, we propose a deep learning-based framework for automated assessment of the quality of physical rehabilitation exercises.
A Deep Learning Framework for Assessing Physical Rehabilitation. from images.deepai.org
The article proposes a new framework for assessment of physical rehabilitation exercises based on a deep learning approach. The objective of the framework is automated.
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The article proposes a deep learning-based framework for assessment of rehabilitation exercises. To this end, an autoencoder NN is employed for reducing the.
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This work established a supervised learning method to automatically assess physical rehabilitation exercises in the home environment using computer vision using a graph.
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In this paper, we propose a deep learning-based framework for automated assessment of the quality of physical rehabilitation exercises. The main components of the.
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DOI: 10.1109/TNSRE.2020.2966249 Corpus ID: 59413855. A Deep Learning Framework for Assessing Physical Rehabilitation Exercises @article{Liao2020ADL, title={A Deep Learning.
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DOI: 10.1109/TNSRE.2020.2966249 Corpus ID: 59413855; A Deep Learning Framework for Assessing Physical Rehabilitation Exercises @article{Liao2020ADL, title={A Deep Learning.
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Despite the essential role of rehabilitation assessment toward improved patient outcomes and reduced healthcare costs, existing approaches lack versatility, robustness, and practical.
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A-Deep-Learning-Framework-for-Assessing-Physical-Rehabilitation-Exercises. The codes in this repository are based on the eponymous research project A Deep Learning Framework for.
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The proposed deep spatio-temporal neural network arranges data into temporal pyramids, and exploits the spatial characteristics of human movements by using sub-networks.
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The presented framework is validated using a dataset of ten rehabilitation exercises. The significance of this work is that it is the first that implements deep neural networks for.
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framework for computer-aided assessment of rehabilitation exercises; (2) A deep spatio-temporal NN model for outputing movement quality scores; and (3) A performance metric that
