Paper Info
Title
A Novel Approach In Combination Of 3d Gait Analysis Data For Aiding Clinical Decision-Making In Patients With Parkinson'S Disease
Abstract
The most common methods used by neurologist to evaluate Parkinson's Disease (PD) patients are rating scales, that are affected by subjective and non-repeatable observations. Since several research studies have revealed that walking is a sensitive indicator for the progression of PD. In this paper, we propose an innovative set of features derived from three-dimensional Gait Analysis in order to classify motor signs of motor impairment in PD and differentiate PD patients from healthy subjects or patients suffering from other neurological diseases. We consider kinematic data from Gait Analysis as Gait Variables Score (GVS), Gait Profile Score (GPS) and spatio-temporal data for all enrolled patients. We then carry out experiments evaluating the extracted features using an Artificial Neural Network (ANN) classifier. The obtained results are promising with the best classifier score accuracy equal to 95.05%.
Year
DOI
Venue
2017
10.1007/978-3-319-63312-1_44
INTELLIGENT COMPUTING THEORIES AND APPLICATION, ICIC 2017, PT II
Keywords
Field
DocType
Parkinson's disease, Gait analysis, Artificial neural network, Classification
Parkinson's disease,Disease,Clinical decision making,Computer science,Rating scale,Gait analysis,Artificial intelligence,Artificial neural network,Machine learning
Conference
Volume
ISSN
Citations 
10362
0302-9743
2
PageRank 
References 
Authors
0.39
8
10
Name
Order
Citations
PageRank
Ilaria Bortone1274.11
Francesco Trotta2779.32
Antonio Brunetti3354.22
Giacomo Donato Cascarano453.16
Claudio Loconsole58714.19
Nadia Agnello621.07
Alberto Argentiero721.07
Giuseppe Nicolardi820.39
Antonio Frisoli947665.01
V. Bevilacqua109610.56