ANALYSIS OF HUMAN HIP MOVEMENT USING NONLINEAR TIMESERIES ANALYSIS METHODS

Marius GEORGESCU, Daniela TARNITA, Ilie DUMITRU, Alin PETCU, Razvan VADUVA, Dan MARGHITU

Abstract


In this paper, measurements were acquired from a group of seven subjects with healthy right hips and left hips affected by an osteoarthritic (OA) process in first stage of evolution. The measurements consist in 126 time-series, where values represent the angles of the hip joint in sagittal plane, describing the flexion-extension movement of each hip joint, of each subject performing nine experimental tests of walking on treadmill, at three predefined speeds and three predefined incline angles. Using the experimental time-series, a measure of the local dynamic stability was estimated with Lyapunov exponents.


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Stergiou, N., Harbourne, RT., Cavanaugh, J., Optimal movement variability: A new theoretical perspective for neurologic physical therapy. Journal of Neurologic Physical Therapy, 30, 120–129, 2006.

Dingwell, J.,Cusumano, J.,Cavanagh, P., Sternad, D., Local Dynamic Stability Versus Kinematic Variability of Continuous Overground and Treadmill Walking, Journal of biomechanical engineering, vol. 123, pp. 27–32, 2001.

Tarnita, D., Marghitu, D.B., Nonlinear dynamics of normal and osteoarthritic human knee, Proceedings of the Romanian Academy, series A. 18(4), pp. 353-360, 2017

England, S., Granata, K., The influence of gait speed on local dynamic stability of walking, Gait & posture, vol. 25, pp. 172–178, 2007.

Tarnita, D. N., Georgescu, M., Tarnita, D.N., Application of Nonlinear Dynamics to Human Knee Movement on Plane and Inclined Treadmill Mechanisms and Machine Science, vol. 39, pp. 59–73, 2016.

Mehdizadeh, S., Arshi, A., Davids, K., Effect of speed on local dynamic stability of locomotion under different task constraints in running, European Journal of Sport Science, vol 14 pp 791-798 2014

Tarnita, D., Georgescu, M., et al., Nonlinear Analysis of Human Ankle Dynamics, Mechanisms and machine science, vol 65, pp. 235–243, 2019.

Georgescu, M., Petcu, A., Tarnita, D., Influences of Speed and Treadmill Inclination on the Local Dynamic Stability of Human Knee Joint, Applied Mechanics and Materials, vol. 880, pp. 130–135, 2018.

Tarnita, D., Pisla, D., Geonea, I., Vaida, C., et al., Static and Dynamic Analysis of Osteoarthritic and Orthotic Human Knee, Journal of Bionic Engineering, vol. 16, pp. 514–525, May 2019.

Geonea, I., Tarnita, D., Design and evaluation of a new exoskeleton for gait rehabilitation, Mechanical Sciences, vol. 8, pp. 307–321, Oct. 2017.

Dumitru, N., Copilusi, C., Geonea, I., et al., Dynamic Analysis of an Exoskeleton New Ankle Joint Mechanism, vol. 24, 2015, pp. 709–717.

Tarnita, D., Catana, M., Dumitru, N., Tarnita, D.N, Design and Simulation of an Orthotic Device for Patients with Osteoarthritis, vol. 38, pp. 61–77, 2016.

Vaida, C., et al., Systematic Design of a Parallel Robotic System for Lower Limb Rehabilitation, IEEE Access, vol. 8, pp. 34522-34537, 2020.

Gherman, B., Birlescu, I., Plitea, et al., On the singularity-free workspace of a parallel robot for lower-limb rehabilitation, Proceedings of the Romanian Academy- Series A, pp. 383–391, 2019.

Husty, M., Birlescu, I., Tucan, P., et al., An algebraic parameterization approach for parallel robots analysis, Mechanism and Machine Theory, vol.140, pp.245-257, 2019

Vaida, C., Carbone, G., Major, K., et al, On human robot interaction modalities in the upper limb rehabilitation after stroke, Acta Technica Napocensis, 60(1), pp.91-102, 2017

www.biometricsltd.com

Tarnita, D., Geonea, I.,et al., Numerical Simulations and Experimental Human Gait Analysis Using Wearable Sensors, Mechanisms and Machine Science, 48, pp. 289-304, 2018.

Tarnita, D., Geonea, I., et al., Experimental Characterization of Human Walking on Stairs Applied to Humanoid Dynamics, Mechanisms and machine science, vol. 540. pp.293-301, 2017.

Berceanu, C., Tarnita, D., Filip, D., About an experimental approach used to determine the kinematics of the human, Journal of Solid State Phenomena, Robotics and Automation Systems, vol. 166-167, pp.45-50, 2010.

Tarnita, D., Tarnita, D.N., Bizdoaca, N., Popa, D., Contributions on the dynamic simulation of the virtual model of the human knee joint, Materialwissenschaft und Werkstofftechnik,Vol.40(1-2),pp73-81, 2009

Golyandina, N., Zhigljavsky, A., Singular Spectrum Analysis for Time Series. Springer, Berlin, ISBN 978-3-662-62435-7, 2018.

Golyandina, N., Korobeynikov, A., Zhigljavsky, A., Singular Spectrum Analysis with R. Springer, Berlin, ISBN 978-3-662-57378-5, 2018.

Schreiber, T., Schmitz, A., Classification of Time Series Data with Nonlinear Similarity Measures, Physical Review Letters - PHYS REV LETT, vol. 79, pp. 1475–1478, 1997.

Schreiber, T., Schmitz, A., Surrogate Time Series, Physica D: Nonlinear Phenomena, vol.142, pp. 346–382, 2000.

Huffaker, R., Bittelli, M., Rosa, R., Nonlinear time series analysis with R. Oxford University Press, Oxford, ISBN 978-0198808251, 2017.

Tsay, R. S., Chen, R., Nonlinear time series analysis, Wiley, New Jersey, ISBN: 978-1-119-26407-1, 2018.

Small, M., Tse, C., Applying the method of surrogate data to cyclic time series, Physica D: Nonlinear Phenomena, vol. 164, pp. 187–201, 2002.

Takens, F., Detecting strange attractors in turbulence, in Dynamical Systems and Turbulence, Warwick 1980, pp.366-381, 1981

Hegger, R., Kantz, H., Schreiber, T., Practical implementation of nonlinear time series methods: The TISEAN package, Chaos, vol. 9, no. 2, pp. 413–435, 1999.

Fraser, A. M., Swinney, H.L., Independent coordinates for strange attractors from mutual information, Physical Review A vol.33, no. 2, pp.1134–1140, 1986

Packard, N. H., Crutchfield, et al., Geometry from a time series, Physical Review Letters, vol. 45, no. 9, pp. 712–716, 1980.

Kennel, M. B., Brown, R., Abarbanel, H.D.I., Determining embedding dimension for phase-space reconstruction using a geometrical construction, Physical Review A, vol. 45, no. 6, pp. 3403–3411, 1992.

Rosenstein, M. T., Collins, J. J., De Luca, C. J., A practical method for calculating largest Lyapunov exponents from small data sets, Physica D: Nonlinear Phenomena, vol. 65, no. 1–2, pp. 117–134, 1993.


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