A hybrid spatio-temporal model for detection and severity rating of Parkinson’s Disease from gait data
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A hybrid spatio-temporal model for detection and severity rating of Parkinson’s Disease from gait data. / Zhao, Aite; Qi, Lin; Li, Jie; Dong, Junyu; Yu, Hui.
In: Neurocomputing, Vol. 315, 13.11.2018, p. 1-8.Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - A hybrid spatio-temporal model for detection and severity rating of Parkinson’s Disease from gait data
AU - Zhao, Aite
AU - Qi, Lin
AU - Li, Jie
AU - Dong, Junyu
AU - Yu, Hui
N1 - 12 month embargo.
PY - 2018/11/13
Y1 - 2018/11/13
N2 - 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.
AB - 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.
KW - Parkinson's disease
KW - diagnosis
KW - gait
KW - temporal data
KW - LSTM
KW - CNN
U2 - 10.1016/j.neucom.2018.03.032
DO - 10.1016/j.neucom.2018.03.032
M3 - Article
VL - 315
SP - 1
EP - 8
JO - Neurocomputing
JF - Neurocomputing
SN - 0925-2312
ER -
ID: 10177531