2020
DOI: 10.3389/fphys.2020.587057
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Parkinson’s Disease Diagnosis and Severity Assessment Using Ground Reaction Forces and Neural Networks

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Cited by 47 publications
(25 citation statements)
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“…However, CNN and transfer learning techniques were not limited to imaging data; they also learn complex features from voices and signal data [ 29 ]. Numerous studies used the biomedical voice ( n = 21) [ 4 , 6 , 22 , 23 , 29 , 33 , 44 , 48 , 50 , 52 , 53 , 55 , 60 , 61 , 73 , 74 , 84 , 93 , 100 , 104 , 105 ] and biometric signal ( n = 14) [ 26 , 31 , 34 , 36 , 45 , 46 , 57 , 62 , 64 , 65 , 68 , 89 , 96 , 98 ]; a few of the included studies used EEG and EMG signals ( n = 5) [ 32 , 39 , 51 , 83 , 85 ].…”
Section: Resultsmentioning
confidence: 99%
“…However, CNN and transfer learning techniques were not limited to imaging data; they also learn complex features from voices and signal data [ 29 ]. Numerous studies used the biomedical voice ( n = 21) [ 4 , 6 , 22 , 23 , 29 , 33 , 44 , 48 , 50 , 52 , 53 , 55 , 60 , 61 , 73 , 74 , 84 , 93 , 100 , 104 , 105 ] and biometric signal ( n = 14) [ 26 , 31 , 34 , 36 , 45 , 46 , 57 , 62 , 64 , 65 , 68 , 89 , 96 , 98 ]; a few of the included studies used EEG and EMG signals ( n = 5) [ 32 , 39 , 51 , 83 , 85 ].…”
Section: Resultsmentioning
confidence: 99%
“…For the popular PhysioNet dataset, Balaji et al ( 2020 ) utilized four machine learning classifiers to stage PD based on force sensing data, in which DT achieved an accuracy of 99.4% in predicting UPDRS scores. El et al ( 2020 ) and Veeraragavan et al ( 2020 ) took advantage of 1D-CNN and ANN for PD staging, respectively, and achieved similar performance. In order to completely utilize the long-term temporal dependencies in the gait data, Balaji et al ( 2021 ) employed the LSTM model for PD staging, which reached an accuracy of 96.6% on UPDRS and H&Y scores.…”
Section: Toward Automatic Recognition In Pd Based On Gait Datamentioning
confidence: 99%
“…For example, sequential tasks of turning and passing through narrow doors have been designed to provoke episodic FoG symptom in PD (Ziegler et al, 2010;Reches et al, 2020). In addition, kinematic parameters such as knee joint angles or range of motion (ROM) as well as kinetic parameters such as ground reaction forces (GRF) can be extracted from certain portable systems to monitor disease progression (Baker, 2013;Veeraragavan et al, 2020). A list of frequently reported gait and balance parameters is presented in Table 1.…”
Section: Gait Measuresmentioning
confidence: 99%
“…Recently, Ghislieri et al (2021) used foot-switch sensors to assess gait parameters in PD patients and age-matched controls during walking and reported a 42%-increase in atypical gait cycles in PD, which correlated with motor symptom severity 1 . Veeraragavan et al (2020) showed that a neural network approach with features extracted from the vertical ground reaction force can differentiate PD from controls as well as predict disease severity (Hoehn & Yahr stage). Furthermore, postural instability is increased in early PD and deteriorates within 12 months of diagnosis, thus providing a potential marker for motor function decline (Mancini et al, 2011(Mancini et al, , 2012a.…”
Section: Measures/biomarkers In Pdmentioning
confidence: 99%