Cyclogram and cross correlation: A comparative study to quantify gait coordination in mental state
Deepak Joshi, Sneh Anand
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DOI: 10.4236/jbise.2010.33044   PDF    HTML     6,764 Downloads   11,126 Views   Citations

Abstract

The purpose of this study to evaluate the effect of mental task on gait coordination. The comparison between two techniques Crosscorrelation and Cyclo- gram has been performed. A set of gait experiments was developed and conducted to evaluate the effect of mental task on gait coordination. The perimeter derived from the geometric figure, cyclogram perimeter (CP), of the knee-knee cyclogram is the main descriptor considered in this study. For crosscorrelation it is the peak value of cross correlation coefficient (CCC) that has been taken for comparison. The sensitivity of both the techniques in terms of percentage has been calculated. Crosscorrelation is highly sensitive (mean=20.4 S.D.=2.3), towards the change in gait coordination with mental task, in comparison to cyclogram perimeter (mean=2.2 S.D.=1.2). The results have strength to assess the progress of rehabilitation among Parkinson patients.

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Joshi, D. and Anand, S. (2010) Cyclogram and cross correlation: A comparative study to quantify gait coordination in mental state. Journal of Biomedical Science and Engineering, 3, 322-326. doi: 10.4236/jbise.2010.33044.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Lord, S. and Rochseter, L. (2007) Walking in real world: Concepts related to functional gait. NZ Journal of Physiotherapy, 35(3).
[2] Satoh, M. and Kuzuhara, S. (2008) Training in mental singing while walking improves gait disturbance in parkinson’s patients. Journal of European Neurology, 60(5).
[3] Wannier, T., Bastiaanse, C., Colombo, G. and Dietz, V. (2001) Arm to leg coordination in humans during walking, creeping and swimming activities. Experimental Brain Research, 141, 375-379.
[4] o’Shea, S., Morris, M.E. and Iansek, R. (2002) Dual task interference during gait in people with parkinson disease: Effects of motor versus cognitive secondary tasks. Journal of Physical Therapy, 82( 9).
[5] Patrick, H., Jaket, C., et al. (2000) Interference between gait and coginitive tasks in a rehabilitation neurological application. Journal of Neurol Neurosurg Psychitary, 69, 479-486.
[6] Camicioli, R., Howieson, D., et al. (1997) Talking while walking: The effect of a dual task in aging and Alzheimer’s disease. Journal of Neurology, 48(4), 955-958.
[7] Lord, S.E., Rochester, L., et al. (2006) The effect of environment and task on gait parameters after stroke: A randomized comparison of measurement condtions. Archives of Physical Medicine and Rehabilitation, 87(7), 967-973.
[8] Armieri, A., et al. (2009) Dual task performance in a healthy young adult population: Results from a symmetric manipulation of task complexity and articulation. Gait and Posture, 29, 346-348.
[9] Li, L. and Caldwell, G.E. (1999) Coefficient of cross correlation and the time domain correspondence. Journal of Electromyography and Kinesiology, 9, 385-389.
[10] Piek, J.P. (1996) A quantitative analysis of spontaneous kicking in two-month-old infants. Human Movement Science 15, 707-726.
[11] Wren, T.A.L., et al. (2006) Cross-correlation as a method for comparing dynamic electromyography signals during gait. Journal of Biomechanics, 39, 2714-2718.
[12] Haddad, J.M., et al. (2006) Adaptations in interlimb and intralimb coordination to asymmetrical loading in human walking. Gait and Posture, 23, 429-434.
[13] Goswami, A. (1998) A new gait parameterization technique by means of cyclogram moments: Application to human slope walking. Gait and Posture, 8, 15-36.
[14] Jiang,X.Y. and Bunke, H. (1991) Simple and fast computation of moments. Pattern Recognition, 24(8), 801- 806.
[15] Yang, L. and Albregtsen, F. (1996) Fast and exact computation of cartesian geometric moments usig discrete green’s theorem. Pattern Recognition, 29(7), 1061-1073.
[16] Charteris, J., Leach, D. and Taves, C. (1979) Comparative kinematic analysis of bipedal and quadrupedal locomotion: A cyclographic technique. Jornal of Analomy, 128(4), 803-819.
[17] Barton, J.G. and Lees, A. (1997) An application of neural networks for distinguishing gait patterns on the basis of hip-knee joint angle diagrams. Gait and posture, 5, 28-33.
[18] Ma, Y.L., Pollick, F.E. and Turner, M. (2005) A statistical approach to gait recognition and verification by using cyclogram. IEEE International Conference on Visual Information Engineering, 425-432.
[19] Hollerbach, J.M., et al. (2001) Torso Force Feedback Realistically Simulates Slope on Treadmill Style Locomotion Interfaces. The International Journal of Robotics Research, 20, 939-951.
[20] Joshi, D., et al. (2009) Gait Co-ordination: Potential marker for mental state. 2nd International Conference in Biomedical Informatics and Signal Processing, 12-14.
[21] Popovic, D. and Jonic, S. (1998) Determining synergy between joint angles during locomotion by radial basis function neural networks. Proceedings of the 20th Annual Conference of the IEEE Engineering on Medicine and Biology Society, 20( 5).
[22] Dejnabadi, H., Jolles, B.M. and Aminian, K. (2008) A new approach for quantitative analysis of inter-Joint coordination during gait. IEEE Transactions on Biomedical Engineering, 55( 2).

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