A vision-based regression model to evaluate Parkinsonian gait from monocular image sequences

  • Authors:

  • You-Yin Chen;Chien-Wen Cho;Sheng-Huang Lin;Hsin-Yi Lai;Yu-Chun Lo;Shin-Yuan Chen;Yuan-Jen Chang;Wen-Tzeng Huang;Chin-Hsing Chen;Fu-Shan Jaw;Siny Tsang;Sheng-Tsung Tsai

  • Affiliations:

  • Department of Electrical Engineering, National Chiao Tung University, No. 1001, Ta-Hsueh Rd., Hsinchu City 300, Taiwan, ROC;Department of Electrical Engineering, National Chiao Tung University, No. 1001, Ta-Hsueh Rd., Hsinchu City 300, Taiwan, ROC;Department of Neurology, Tzu Chi General Hospital, Tzu Chi University, No. 707, Sec. 3, Chung Yang Rd., Hualien 970, Taiwan, ROC and Institute of Biomedical Engineering, College of Medicine, Natio ...;Department of Electrical Engineering, National Chiao Tung University, No. 1001, Ta-Hsueh Rd., Hsinchu City 300, Taiwan, ROC;Institute of Biomedical Engineering, College of Medicine, National Taiwan University, No. 1, Sec. 1, Jen-Ai Rd., Taipei 100, Taiwan, ROC;Department of Neurosurgery, Tzu Chi General Hospital, Tzu Chi University, No. 707, Sec. 3, Chung Yang Rd., Hualien 970, Taiwan, ROC;Department of Management Information Systems, Central Taiwan University of Science and Technology, No. 666, Buzih Rd., Taichung 406, Taiwan, ROC;Department of Computer Science and Information Engineering, Minghsin University of Science and Technology, No. 1, Xinxing Rd., Hsinchu City 304, Taiwan, ROC;Department of Management Information Systems, Central Taiwan University of Science and Technology, No. 666, Buzih Rd., Taichung 406, Taiwan, ROC;Institute of Biomedical Engineering, College of Medicine, National Taiwan University, No. 1, Sec. 1, Jen-Ai Rd., Taipei 100, Taiwan, ROC;Department of Psychology, University of Virginia, 102 Gilmer Hall, P.O. Box 400400, Charlottesville, VA 22904-4400, USA;Department of Neurosurgery, Tzu Chi General Hospital, Tzu Chi University, No. 707, Sec. 3, Chung Yang Rd., Hualien 970, Taiwan, ROC

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2012

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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