Title: A vision-based regression model to evaluate Parkinsonian gait from monocular image sequences
Authors: Chen, You-Yin
Cho, Chien-Wen
Lin, Sheng-Huang
Lai, Hsin-Yi
Lo, Yu-Chun
Chen, Shin-Yuan
Chang, Yuan-Jen
Huang, Wen-Tzeng
Chen, Chin-Hsing
Jaw, Fu-Shan
Tsang, Siny
Tsai, Sheng-Tsung
Department of Electrical and Computer Engineering
Keywords: Human motion analysis;Parkinsonian gait;Linear discriminant analysis (LDA);Classification;Regression
Issue Date: 1-Jan-2012
Abstract: Parkinson's Disease (PD) is a common neurodegenerative disorder with progressive loss of dopaminergic and other sub-cortical neurons. Among various approaches, gait analysis is commonly used to help identify the biometric features of PD. There have been some studies to date on both the classification of PD and estimation of gait parameters. However, it is also important to construct a regression system that can evaluate the degree of abnormality in PD patients. In this paper, we intended to develop a PD gait regression model that is capable of predicting the severity of motor dysfunction from given gait image sequences. We used a model-free strategy and thus avoided the critical demands of segmentation and parameter estimation. Furthermore, we used linear discriminant analysis (LDA) to increase the feature efficiency by maximizing and minimizing the between- and within-group variations. Regression was also achieved by assessing the spatial and temporal information through classification and finally by using these two new indices for linear regression. According to the experiments, the outcomes significantly correlated with the sum of sub-scores from the Unified Parkinson's Disease Rating Scale (UPDRS): motor examination section with r = 0.92 and 0.85 for training and testing, respectively, with p < 0.0001. Compared with conventional methods, our system provided a better evaluation of PD abnormality. (C) 2011 Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.eswa.2011.07.042
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2011.07.042
Volume: 39
Issue: 1
Begin Page: 520
End Page: 526
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