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A hybrid spatio-temporal model for detection and severity rating of Parkinson’s Disease from gait data

Research output: Contribution to journalArticle

When diagnosing Parkinson’s disease (PD), medical specialists normally assess several clinical manifestations of the PD patient and rate a severity level according to established criteria. This rating process is highly depended by doctors’ expertise, which is subjective and inefficient. In this paper, we propose a machine learning based method to automatically rate the PD severity from gait information, in particular, the sequential data of Vertical Ground Reaction Force (VGRF) recorded by foot sensors. We developed a two-channel model that combines Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) to learn the spatio-temporal patterns behind the gait data. The model was trained and tested on three public VGRF datasets. Our proposed method outperforms existing ones in terms of prediction accuracy of PD severity levels. We believe the quantitative evaluation provided by our method will benefit clinical diagnosis of Parkinson’s disease.
Original languageEnglish
JournalNeurocomputing
Early online date20 Mar 2018
DOIs
StateEarly online - 20 Mar 2018

Documents

  • hybrid-spatio-temporal_Neurocomputing2018

    Accepted author manuscript (Post-print), 1006 KB, PDF-document

    Due to publisher’s copyright restrictions, this document is not freely available to download from this website until: 20/03/19

    License: CC BY-NC-ND

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