Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Avidan S (2005) Ensemble tracking. IEEE Trans Pattern Anal Machine Intell 29:261–271

    Article  Google Scholar 

  2. Benedetti M, Catani F, Leardini A, Pignotti E, Giannini S (1998) Data management in gait analysis for clinical applications. Clin Biomech 13(3):204–215

    Article  Google Scholar 

  3. Bharatkumar A, Daigle K, Pandy M, Cai Q, Aggarwal J Lower limb kinematics of human walking with the medial axis transformation. In: Motion of non-rigid and articulated objects, 1994. In: Proceedings of the 1994 IEEE workshop on, 1994. IEEE, pp 70–76

  4. Bilney B, Morris M, Webster K (2003) Concurrent related validity of the GAITRite® walkway system for quantification of the spatial and temporal parameters of gait. Gait Posture 17(1):68–74

    Article  PubMed  Google Scholar 

  5. Breniere Y, Cuong Do M, Bouisset S (1987) Are dynamic phenomena prior to stepping essential to walking? J Mot Behav 19(1):62–76

    Article  CAS  PubMed  Google Scholar 

  6. Chang H, C T, Lee S, Liao S, Lee H, Shih Y, Cheng H (2004) Temporal differences in relative phasing of gait initiation and first step length in patients with cervical and lumbosacral spinal cord injuries. Spinal Cord 42(5):281–289

    Article  CAS  PubMed  Google Scholar 

  7. Chen Z, Lee HJ (1992) Knowledge-guided visual perception of 3-D human gait from a single image sequence. IEEE Trans Syst Man Cybern 22(2):336–342

    Article  Google Scholar 

  8. Chien S-L, Lin S-Z, Liang C-C, Soong Y-S, Lin S-H, Hsin Y-L, Lee C-W, Chen S-Y (2006) The efficacy of quantitative gait analysis by the GAITRite system in evaluation of parkinsonian bradykinesia. Parkinsonism Relat Disord 12(7):438–442

    Article  PubMed  Google Scholar 

  9. Cho C, Kunin M, Kudo K, Osaki Y, Olanow CW, Cohen B, Raphan T (2010) Frequency-velocity mismatch: a fundamental abnormality in parkinsonian gait. J Neurophysiol 103(3):1478–1489

    Article  PubMed  PubMed Central  Google Scholar 

  10. Comaniciu D, Ramesh V, Meer P (2000) Real-time tracking of non-rigid objects using mean shift. In: IEEE computer society conference on computer vision and pattern recognition (CVPR’00), 2000. Published by the IEEE Computer Society, p 2142

  11. Crenna P, Carpinella I, Rabuffetti M, Rizzone M, Lopiano L, Lanotte M, Ferrarin M (2006) Impact of subthalamic nucleus stimulation on the initiation of gait in Parkinson’s disease. Exp Brain Res 172(4):519–532

    Article  CAS  PubMed  Google Scholar 

  12. Djaldetti R, Ziv I, Melamed E (2006) The mystery of motor asymmetry in Parkinson’s disease. Lancet Neurol 5(9):796–802

    Article  PubMed  Google Scholar 

  13. Gelb DJ, Oliver E, Gilman S (1999) Diagnostic criteria for Parkinson disease. Arch Neurol 56(1):33–39

    Article  CAS  PubMed  Google Scholar 

  14. Green RD, Guan L (2004) Quantifying and recognizing human movement patterns from monocular video images-part i: a new framework for modeling human motion. IEEE Trans Circuits Syst Video Technol 14(2):179–190

    Article  Google Scholar 

  15. Green RD, Guan L, Burne J (2000) Video analysis of gait for diagnosing movement disorders. J Electron Imaging 9(1):16–21

    Article  Google Scholar 

  16. Halliday SE, Winter DA, Frank JS, Patla AE, Prince F (1998) The initiation of gait in young, elderly, and Parkinson’s disease subjects. Gait Posture 8(1):8–14

    Article  PubMed  Google Scholar 

  17. Hoehn MM, Yahr MD (1967) Parkinsonism: onset, progression and mortality. Neurology 17(5):427–442

    Article  CAS  PubMed  Google Scholar 

  18. Jankovic J, McDermott M, Carter J, Gauthier S, Goetz C, Golbe L, Huber S, Koller W, Olanow C, Shoulson I (1990) Variable expression of Parkinson's disease: a base-line analysis of the DATATOP cohort. The Parkinson Study Group. Neurology 40(10):1529–1534

    CAS  Google Scholar 

  19. Jasiewicz JM, Allum JHJ, Middleton JW, Barriskill A, Condie P, Purcell B, Li RCT (2006) Gait event detection using linear accelerometers or angular velocity transducers in able-bodied and spinal-cord injured individuals. Gait Posture 24(4):502–509

    Article  PubMed  Google Scholar 

  20. Lang A, Lozano A (1998) Parkinson’s disease. Second of two parts. N Engl J Med 339(16):1130–1143

    Article  CAS  PubMed  Google Scholar 

  21. Langston JW, Widner H, Goetz CG, Brooks D, Fahn S, Freeman T, Watts R (1992) Core assessment program for intracerebral transplantations (CAPIT). Mov Disord 7(1):2–13

    Article  CAS  PubMed  Google Scholar 

  22. Martinez Martin P, Gil Nagel A, Gracia LM, Gomez JB, Martinez Sarries J, Bermejo F (1994) Unified Parkinson’s disease rating scale characteristics and structure. Mov Disord 9(1):76–83

    Article  CAS  PubMed  Google Scholar 

  23. McDonough AL, Batavia M, Chen FC, Kwon S, Ziai J (2001) The validity and reliability of the GAITRite system’s measurements: a preliminary evaluation. Arch Phys Med Rehabil 82(3):419–425

    Article  CAS  PubMed  Google Scholar 

  24. Moore ST, MacDougall HG, Ondo WG (2008) Ambulatory monitoring of freezing of gait in Parkinson’s disease. J Neurosci Methods 167(2):340–348

    Article  PubMed  Google Scholar 

  25. Morris ME, Matyas TA, Iansek R, Summers JJ (1996) Temporal stability of gait in Parkinson’s disease. Phys Ther 76(7):763–777

    CAS  PubMed  Google Scholar 

  26. Morris M, Iansek R, Matyas T, Summers J (1998) Abnormalities in the stride length-cadence relation in parkinsonian gait. Mov Disord 13(1):61–69

    Article  CAS  PubMed  Google Scholar 

  27. Nallegowda M, Singh U, Handa G, Khanna M, Wadhwa S, Yadav SL, Kumar G, Behari M (2004) Role of sensory input and muscle strength in maintenance of balance, gait, and posture in Parkinson’s disease: a pilot study. Am J Phys Med Rehabil 83(12):898

    Article  PubMed  Google Scholar 

  28. Okuno R, Fujimoto S, Akazawa J, Yokoe M, Sakoda S, Akazawa K (2008) Analysis of spatial temporal plantar pressure pattern during gait in Parkinson’s disease. In: 30th annual international conference of the IEEE Vancouver, BC, 2008. IEEE, pp 1765–1768

  29. Rosin R, Topka H, Dichgans J (1997) Gait initiation in Parkinson’s disease. Mov Disord 12(5):682–690

    Article  CAS  PubMed  Google Scholar 

  30. Selby-Silverstein L, Besser M (1999) Accuracy of the GAITRite system for measuring temporal-spatial parameters of gait. Phys Ther 79(5):S59

    Google Scholar 

  31. Sofuwa O, Nieuwboer A, Desloovere K, Willems AM, Chavret F, Jonkers I (2005) Quantitative gait analysis in Parkinson’s disease: comparison with a healthy control group. Arch Phys Med Rehabil 86(5):1007–1013

    Article  PubMed  Google Scholar 

  32. Švehlík M, Zwick EB, Steinwender G, Linhart WE, Schwingenschuh P, Katschnig P, Ott E, Enzinger C (2009) Gait analysis in patients with parkinson’s disease off dopaminergic therapy. Arch Phys Med Rehabil 90(11):1880–1886

    Article  PubMed  Google Scholar 

  33. Wen Z, Cai Z (2007) A robust object tracking approach using mean shift. In: Proceedings of the 3rd international conference on natural computation, vol 2, pp 170–174

  34. Yang YR, Lee YY, Cheng SJ, Lin PY, Wang RY (2008) Relationships between gait and dynamic balance in early Parkinson’s disease. Gait Posture 27(4):611–615

    Article  CAS  PubMed  Google Scholar 

  35. Yilmaz A (2007) Object tracking by asymmetric kernel mean shift with automatic scale and orientation selection. In: IEEE conference on computer vision and pattern recognition, Minneapolis, MN, USA 2007

  36. Zhiqiang W, Zixing C (2007) A robust object tracking approach using mean shift. In: International conference on natural computation, pp 170–174

Download references

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.

Author information

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

Authors
  1. Sheng-Huang Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shih-Wei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yu-Chun Lo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hsin-Yi Lai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chich-Haung Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shin-Yuan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yuan-Jen Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Chin-Hsing Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Wen-Tzeng Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Fu-Shan Jaw
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. You-Yin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Siny Tsang
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Lun-De Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to You-Yin Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

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

Keywords

Search

Navigation