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

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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.

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Acknowledgments

The authors are grateful to Chung-Yi Wu for his technological support during this study. We are also very grateful to the volunteers who generously gave their time to assist with this research. This research is supported by the Grant Nos. 101-2622-E-010-002-CC2 and 102-2221-E-010-011-MY3 from the Ministry of Science and Technology of the Republic of China in Taiwan.

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Authors and Affiliations

  1. Institute of Biomedical Engineering, College of Medicine, National Taiwan University, No. 1, Sec. 1, Jen-Ai Rd., Taipei, 100, Taiwan, ROC

    Sheng-Huang Lin

  2. Department of Neurology, Buddhist Tzu Chi General Hospital, Buddhist Tzu Chi University, No. 707, Sec. 3, Chung Yang Rd., Hualien, 970, Taiwan, ROC

    Sheng-Huang Lin

  3. Department of Electrical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 106, Taiwan, ROC

    Shih-Wei Chen

  4. Center for Optoelectronic Biomedicine, National Taiwan University College of Medicine, No. 1, Sec. 1, Jen-Ai Rd., Taipei, 100, Taiwan, ROC

    Yu-Chun Lo

  5. Qiushi Academy for Advanced Studies (QAAS), Zhejiang University Interdisciplinary Institute of Neuroscience and Technology (ZIINT), No. 268, Kaixuan Rd., Hangzhou City, 310029, Zhejiang, China

    Hsin-Yi Lai

  6. Department of Physical Therapy, College of Medicine, Buddhist Tzu Chi University, No. 701, Sec. 3, Jhongyang Rd., Hualien, 970, Taiwan, ROC

    Chich-Haung Yang

  7. Department of Neurosurgery, Buddhist Tzu Chi General Hospital, Buddhist Tzu Chi University, No. 707, Sec. 3, Chung Yang Rd., Hualien, 970, Taiwan, ROC

    Shin-Yuan Chen

  8. Institute of Biomedical Engineering and Material Science, Central Taiwan University of Science and Technology, No. 666, Buzih Rd., Taichung, 406, Taiwan, ROC

    Yuan-Jen Chang

  9. Department of Management Information System, Central Taiwan University of Science and Technology, No. 666, Buzih Rd., Taichung, 406, Taiwan, ROC

    Yuan-Jen Chang & Chin-Hsing Chen

  10. Department of Computer Science and Information Engineering, Minghsin University of Science and Technology, No. 1, Xin-Xing Rd., Hsinchu, 304, Taiwan 304, ROC

    Wen-Tzeng Huang

  11. Institute of Biomedical Engineering, National Taiwan University, No. 1, Sec. 1, Jen-Ai Rd., Taipei, 100, Taiwan, ROC

    Fu-Shan Jaw

  12. Department of Biomedical Engineering, National Yang Ming University, No. 155, Sec. 2, Linong St., Taipei, 112, Taiwan, ROC

    You-Yin Chen

  13. Department of Psychology, University of Virginia, 102 Gilmer Hall, PO BOX 400400, Charlottesville, VA, 22904-4400, USA

    Siny Tsang

  14. Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, No. 28 Medical Drive, #05-COR, Singapore, 117456, Singapore

    Lun-De Liao

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  1. Sheng-Huang Lin
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Correspondence to You-Yin Chen.

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Lin, SH., Chen, SW., Lo, YC. et al. Quantitative measurement of Parkinsonian gait from walking in monocular image sequences using a centroid tracking algorithm. Med Biol Eng Comput 54, 485–496 (2016). https://doi.org/10.1007/s11517-015-1335-2

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  • DOI: https://doi.org/10.1007/s11517-015-1335-2

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