Quantitative measurement of Parkinsonian gait from walking in monocular image sequences using a centroid tracking algorithm

Sheng Huang Lin, Shih Wei Chen, Yu Chun Lo, Hsin Yi Lai, Chich Haung Yang, Shin Yuan Chen, Yuan Jen Chang, Chin Hsing Chen, Wen Tzeng Huang, Fu Shan Jaw, You Yin Chen, Siny Tsang, Lun De Liao

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Parkinson’s disease (PD) is a neurodegenerative disease of the central nervous system that results from the degeneration of dopaminergic neurons in the substantia nigra. Abnormal gait begins in the early stage and becomes severe as the disease progresses; therefore, the assessment of gait becomes an important issue in evaluating the progression of PD and the effectiveness of treatment. To provide a clinically useful gait assessment in environments with budget and space limitations, such as a small clinic or home, we propose and develop a portable method utilizing the monocular image sequences of walking to track and analyze a Parkinsonian gait pattern. In addition, a centroid tracking algorithm is developed and used here to enhance the method of quantifying kinematic gait parameters of PD in different states. Twelve healthy subjects and twelve mild patients with PD participate in this study. This method requires one digital video camera and subjects with two joint markers attached on the fibula head and the lateral malleolus of the leg. All subjects walk with a natural pace in front of a video camera during the trials. Results of our study demonstrate the stride length and walking velocity significantly decrease in PD without drug compared to PD with drug in both proposed method and simultaneous gait assessment performed by GAITRite® system. In gait initiation, step length and swing velocity also decrease in PD without drug compared to both PD with drug and controls. Our results showed high correlation in gait parameters between the two methods and prove the reliability of the proposed method. With the proposed method, quantitative measurement and analysis of Parkinsonian gait could be inexpensive to implement, portable within a small clinic or home, easy to administer, and simple to interpret. Although this study is assessed Parkinsonian gait, the proposed method has the potential to help clinicians and researchers assess the gait of patients with other neuromuscular diseases, such as traumatic brain injury and stroke patients.

Original languageEnglish
Pages (from-to)485-496
Number of pages12
JournalMedical and Biological Engineering and Computing
Volume54
Issue number2-3
DOIs
Publication statusPublished - Mar 1 2016
Externally publishedYes

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Video cameras
Neurodegenerative diseases
Digital cameras
Neurology
Neurons
Brain
Kinematics

Keywords

  • Centroid tracking algorithm (CTA)
  • Gait analysis
  • Parkinson’s disease (PD)

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computer Science Applications

Cite this

Quantitative measurement of Parkinsonian gait from walking in monocular image sequences using a centroid tracking algorithm. / Lin, Sheng Huang; Chen, Shih Wei; Lo, Yu Chun; Lai, Hsin Yi; Yang, Chich Haung; Chen, Shin Yuan; Chang, Yuan Jen; Chen, Chin Hsing; Huang, Wen Tzeng; Jaw, Fu Shan; Chen, You Yin; Tsang, Siny; Liao, Lun De.

In: Medical and Biological Engineering and Computing, Vol. 54, No. 2-3, 01.03.2016, p. 485-496.

Research output: Contribution to journalArticle

Lin, SH, Chen, SW, Lo, YC, Lai, HY, Yang, CH, Chen, SY, Chang, YJ, Chen, CH, Huang, WT, Jaw, FS, Chen, YY, Tsang, S & Liao, LD 2016, 'Quantitative measurement of Parkinsonian gait from walking in monocular image sequences using a centroid tracking algorithm', Medical and Biological Engineering and Computing, vol. 54, no. 2-3, pp. 485-496. https://doi.org/10.1007/s11517-015-1335-2
Lin, Sheng Huang ; Chen, Shih Wei ; Lo, Yu Chun ; Lai, Hsin Yi ; Yang, Chich Haung ; Chen, Shin Yuan ; Chang, Yuan Jen ; Chen, Chin Hsing ; Huang, Wen Tzeng ; Jaw, Fu Shan ; Chen, You Yin ; Tsang, Siny ; Liao, Lun De. / Quantitative measurement of Parkinsonian gait from walking in monocular image sequences using a centroid tracking algorithm. In: Medical and Biological Engineering and Computing. 2016 ; Vol. 54, No. 2-3. pp. 485-496.
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