Commenced in January 2007
Paper Count: 32131
Comparison of Developed Statokinesigram and Marker Data Signals by Model Approach
Abstract:Background: Based on statokinezigram, the human balance control is often studied. Approach to human postural reaction analysis is based on a combination of stabilometry output signal with retroreflective marker data signal processing, analysis, and understanding, in this study. The study shows another original application of Method of Developed Statokinesigram Trajectory (MDST), too. Methods: In this study, the participants maintained quiet bipedal standing for 10 s on stabilometry platform. Consequently, bilateral vibration stimuli to Achilles tendons in 20 s interval was applied. Vibration stimuli caused that human postural system took the new pseudo-steady state. Vibration frequencies were 20, 60 and 80 Hz. Participant's body segments - head, shoulders, hips, knees, ankles and little fingers were marked by 12 retroreflective markers. Markers positions were scanned by six cameras system BTS SMART DX. Registration of their postural reaction lasted 60 s. Sampling frequency was 100 Hz. For measured data processing were used Method of Developed Statokinesigram Trajectory. Regression analysis of developed statokinesigram trajectory (DST) data and retroreflective marker developed trajectory (DMT) data were used to find out which marker trajectories most correlate with stabilometry platform output signals. Scaling coefficients (λ) between DST and DMT by linear regression analysis were evaluated, too. Results: Scaling coefficients for marker trajectories were identified for all body segments. Head markers trajectories reached maximal value and ankle markers trajectories had a minimal value of scaling coefficient. Hips, knees and ankles markers were approximately symmetrical in the meaning of scaling coefficient. Notable differences of scaling coefficient were detected in head and shoulders markers trajectories which were not symmetrical. The model of postural system behavior was identified by MDST. Conclusion: Value of scaling factor identifies which body segment is predisposed to postural instability. Hypothetically, if statokinesigram represents overall human postural system response to vibration stimuli, then markers data represented particular postural responses. It can be assumed that cumulative sum of particular marker postural responses is equal to statokinesigram.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1129958Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 598
 F.B. Horak, “Postural orientation and equilibrium: what do we need to know about neural control of balance to prevent falls?” Age and Ageing, vol. 35, pp. 7-11, 2006.
 A. Słupik, K. Jaworski, A. Mosiołek, D. Białoszewksi, “Assessment of the impact of regular pilates exercises on static balance in healthy adult women: Preliminary report,” WASET Bioengineering and Life Sciences, vol. 2(7), 2015, pp. 1224.
 J. W. Kim, Y. R. Kwon, Y. J. Ho, H. M. Jeon, G. M. Eom, “Relationship between static balance and body characteristics in the elderly,” WASET Biomedical and biological engineering, vol. 1(10), 2014, pp. 14.
 D. Abrahamova, M. Mancini, F. Hlavacka and L. Chiari, “The age-related changes of trunk responses to Achilles tendon vibration,” Neurosci lett., vol. 467, pp. 220-224, 2009.
 A. Polonyova, F. Hlavacka, “Human postural responses to different frequency vibrations of lower leg muscles,” Phisiol. Res., vol. 50, pp. 405-410, 2001.
 N. Adamcova, F. Hlavacka, “Modification of human postural responses to soleus muscle vibration by rotation of visual scene,” Gait Posture, vol. 25, pp. 99-105, 2007.
 H. Ceyte, C. Cian, R. Zory, P.A. barraud, A. Roux, M. Guerrraz, “Effect of Achilles tendon vibrationn on postural orientation,” Neurosci Lett., vol. 416, pp. 71-75, 2007.
 B. Barbolyas, J. Chrenova, K. Buckova, M. Cekan, B. Hucko and L. Dedik, “Postural system adaptation to Achilles tendon vibration stimuli - The pilot study,” in 7th International Posture Symposium, Slovakia, 2015, to be published.
 E.S. Lee, Quasilinearization and invariant embedding. Academic Press, New York, 1968.
 I. Manno, Introduction to the Monte-Carlo method. Akademiai Kiado, Budapest, 1999.
 H Akaike, Canonical correlation analysis of time series and the use of an information criterion. In: Mehra RK, Lainiotis DG (eds) System Identification: Advances and Case Studies, Academic Press, New York, pp. 27-96, 1976.
 L. Dedík, M. Ďurišová, System approach in technical, environmental, and bio-medical studies. Slovak University of Technology, Bratislava, 1999.