A Novel Model to Generate Heterogeneous and Realistic Time-Series Data for Post-Stroke Rehabilitation Assessment

A Novel Model to Generate Heterogeneous and Realistic Time-Series Data for Post-Stroke Rehabilitation Assessment

The application of machine learning-based tele-rehabilitation faces the challenge of limited availability of data. To overcome this challenge, data augmentation techniques are commonly employed to generate synthetic data that reflect the configurations of real data. One such promising data augmentat...

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Bibliographic Details
Main Authors: Issam Boukhennoufa, Delaram Jarchi, Xiaojun Zhai, Victor Utti, Saeid Sanei, Tracey K. M. Lee, Jo Jackson, Klaus D. McDonald-Maier
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Transactions on Neural Systems and Rehabilitation Engineering
Subjects:
Generative adversarial networks
mode collapse
stroke rehabilitation
time series
Online Access:https://ieeexplore.ieee.org/document/10144395/

Internet

https://ieeexplore.ieee.org/document/10144395/

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