Using Subject-Specific Reference Cyclograms on the Gait Evaluation of a Cerebral Palsy Patient

  • Pedro Sá Cunha
  • João P. FerreiraEmail author
  • A. Paulo Coimbra
  • Manuel M. Crisóstomo
  • César Bouças
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1193)


Cyclograms are parametric curves composed by the angle trajectory curves of two joints and are an easy way of visualizing and condensate information. The features of Hip-Knee cyclograms can be used to evaluate and asses patient deviation from normality and to track treatment progress. Different joints of the patient can be used for the generation of this parametric curves, as well as the same joint for dominant and non-dominant limb. This former type of cyclograms are called bilateral cyclograms and provide insights of patient symmetry through their geometric properties. The present gait evaluation method is based on the comparison of patient cyclograms with healthy subject cyclograms. In order to obtain a reliable comparison, healthy subject-specific cyclograms should be used instead of generic standard cyclograms because joint angle curves are heavily influenced by subject characteristics (age, height, weight) and gait speed. In the present work, subject-specific knee and hip healthy reference curves are generated for a patient diagnosed with Cerebral Palsy using an Extreme Learning Machine. In this way features of importance to patient gait evaluation can be extracted and compared against several healthy reference cyclograms.


ELM Cerebral Palsy Subject-specific profiles Cyclograms 


Compliance with Ethical Standards

Conflict of Interest

All authors declare that they have no conflict of interest.


  1. 1.
    Błażkiewicz, M., Wit, A.: Artificial neural network simulation of lower limb joint angles in normal and impaired human gait. Acta Bioeng. Biomech. 20(3), 43–49 (2018)Google Scholar
  2. 2.
    Kutilek, P., Farkasova, B.: Prediction of lower extremities’ movement by angle-angle diagrams and neural networks (2011)Google Scholar
  3. 3.
    Kutilek, P., Viteckova, S., Svoboda, Z., Smrcka, P.: Kinematic quantification of gait asymmetry in patients with peroneal nerve palsy based on bilateral cyclograms. J. Musculoskelet. Neuronal Interact. 13(2), 244–250 (2013)Google Scholar
  4. 4.
    Sobral, H., et al.: Two new indices to assess gait disturbances applied to anterior cruciate ligament reconstructed knees. In: 8th Annual IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems CYBER 2018, pp. 701–706 (2019)Google Scholar
  5. 5.
    Pilkar, R., Ramanujam, A., Chervin, K., Forrest, G.F., Nolan, K.J.: Cyclogram-based joint symmetry assessment after utilization of a foot drop stimulator during post-stroke hemiplegic gait. J. Biomech. Eng. 140(12), 121005 (2018)CrossRefGoogle Scholar
  6. 6.
    Rozumalski, A., Schwartz, M.H.: The GDI-kinetic: a new index for quantifying kinetic deviations from normal gait. Gait Posture 33(4), 730–732 (2011)CrossRefGoogle Scholar
  7. 7.
    McMulkin, M.L., MacWilliams, B.A.: Application of the gillette gait index, gait deviation index and gait profile score to multiple clinical pediatric populations. Gait Posture 41(2), 608–612 (2015)CrossRefGoogle Scholar
  8. 8.
    Yogev, G., Plotnik, M., Peretz, C., Giladi, N., Hausdorff, J.M.: Gait asymmetry in patients with Parkinson’s disease and elderly fallers: when does the bilateral coordination of gait require attention? Exp. Brain Res. 177(3), 336–346 (2007)CrossRefGoogle Scholar
  9. 9.
    Wafai, L., Zayegh, A., Woulfe, J., Mahfuzul, S., Begg, R.: Identification of foot pathologies based on plantar pressure asymmetry. Sensors (Switzerland) 15(8), 20392–20408 (2015)CrossRefGoogle Scholar
  10. 10.
    Hershler, C., Milner, M.: Angle–angle diagrams in the assessment of locomotion. Am. J. Phys. Med. 59(3), 109–125 (1980)Google Scholar
  11. 11.
    Hershler, C., Milner, M.: Angle-angle diagrams in above-knee amputee and cerebral palsy gait. Am. J. Phys. Med. 59(4), 165–183 (1980)Google Scholar
  12. 12.
    Oberg, K., Lanshammar, H.: An investigation of kinematic and kinetic variables for the description of prosthetic gait using the ENOCH system. Prosthet. Orthot. Int. 6(1), 43–47 (2009)CrossRefGoogle Scholar
  13. 13.
    Vaughan, C.L., Davis, B.L., OConnor, J.C.: Dynamics of Human Gait, vol. 2. Human Kinetics Publishers, Leeds (1992)Google Scholar
  14. 14.
    Sinclair, J., Taylor, P.J., Hobbs, S.J.: Digital filtering of three-dimensional lower extremity kinematics: an assessment. J. Hum. Kinet. 39(1), 25–36 (2013)CrossRefGoogle Scholar
  15. 15.
    Winter, D.A.: Biomechanics and Motor Control of Human Movement, 4th edn. Wiley, Hoboken (2009)CrossRefGoogle Scholar
  16. 16. Gait 2392 and 2354 Models - OpenSim Documentation. Accessed 25 Aug 2019

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Electrical EngineeringSuperior Institute of Eng. of CoimbraCoimbraPortugal
  2. 2.Department of Electrical and Computer Engineering, Institute of Systems and RoboticsUniversity of CoimbraCoimbraPortugal

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