Classification of mild Parkinson’s disease: data augmentation of time-series gait data obtained via inertial measurement units

Abstract Data-augmentation methods have emerged as a viable approach for improving the state-of-the-art performances for classifying mild Parkinson’s disease using deep learning with time-series data from an inertial measurement unit, considering the limited amount of training datasets available in...

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Bibliographic Details
Main Authors: Hirotaka Uchitomi, Xianwen Ming, Changyu Zhao, Taiki Ogata, Yoshihiro Miyake
Format: Article
Language:English
Published: Nature Portfolio 2023-08-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-023-39862-4