Application of the Stability Coefficient for Considering Intrinsic Variability of Postural Sway

  • Dongwon Kang
  • Jeongwoo Seo
  • Junggil Kim
  • Jinsoo Lee
  • Jinseung Choi
  • Gyerae TackEmail author
Regular Paper


Postural sway typically has high intrinsic variability. Due to high intrinsic variability, the reliability of its clinical application is limited. This study proposed the modified stability coefficient considering the intrinsic variability of postural sway for reducing its high level of variability, and calculated the contribution of major sensory systems (somatosensory, visual and vestibular) for its possible clinical application. The subjects of this study were composed of 25 healthy young (HY) adults in their 20 s and 33 healthy older (HO) adults over 65 years of age. Each subject maintained four standing conditions (eyes open and eyes closed on a firm surface, and a foam surface) for 1 min each, and postural sway was measured using the inertial sensor that was attached to their waist. Postural sway was calculated using seven variables that reflect the changes in spatial movements (Mean distance, Root mean square, Path length, Range of acceleration, Mean velocity, Mean frequency, 95% confidence ellipse area). The stability coefficient proposed in this study was calculated using the variables that showed significant difference between groups, and sensory contributions were calculated. The indices on the statistics (p value) and practical significance (effect size: Cohen’s d) between the groups, and the coefficient of variation (CV) within each group were calculated by the calculated sensory contribution. By introducing the stability coefficient, the average CV was reduced to 28.13% in HY and 27.20% in HO with a high level of variation, compared to 36.67% in HY and 39.30% in HO with a very high level of variation. The average CV of sensory ratios was 12.79% in HY and 12.92% in HO with a medium level of variation. As the sensory ratios utilizing stability coefficient show statistical and practical differences of age-related changes in balance and the average CVs with a medium level of variation, these results indicate the possibility of clinical use about the sensory ratios.


Stability coefficient Postural sway Intrinsic variability Inertial sensor Sensory contributions 

List of Symbols


Healthy young


Healthy older


Coefficient of variation


Eyes open, firm surface


Eyes closed, firm surface


Eyes open, foam surface


Eyes closed, foam surface


Mean distance


Root mean square


Path length


Range of acceleration


Mean velocity


Mean frequency


95% confidence ellipse area



This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A2A2A03014511 and 2016R1D1A3B03930135).


  1. 1.
    Pollock, A. S., Durward, B. R., Rowe, P. J., & Paul, J. P. (2000). What is balance? Clinical Rehabilitation, 14, 402–406.CrossRefGoogle Scholar
  2. 2.
    Mancini, M., & Horak, F. B. (2010). The relevance of clinical balance assessment tools to differentiate balance deficits. European Journal of Physical and Rehabilitation Medicine, 46(2), 239–248.Google Scholar
  3. 3.
    Kim, J. W., Chung, H. Y., Kim, C. S., Eom, K. M., et al. (2012). Relationship between body factors and postural sway during natural standing. International Journal of Precision Engineering and Manufacturing, 13(6), 963–968.CrossRefGoogle Scholar
  4. 4.
    Melzer, I., Benjuya, N., & Kaplanski, J. (2004). Postural stability in the elderly: A comparison between fallers and non-fallers. Age and Ageing, 33(6), 602–607.CrossRefGoogle Scholar
  5. 5.
    Nguyen, U. S., Kiel, D. P., Li, W., Galica, A. M., Kang, H. G., Casey, V. A., et al. (2012). Correlations of clinical and laboratory measures of balance in older men and women. Arthritis Care & Research (Hoboken), 64(12), 1895–1902.CrossRefGoogle Scholar
  6. 6.
    Takeshima, N., Islam, M. M., Rogers, M. E., et al. (2014). Pattern of age-associated decline of static and dynamic balance in community-dwelling older women. Geriatrics & Gerontology International, 14, 556–560.CrossRefGoogle Scholar
  7. 7.
    Horak, F. B. (2006). Postural orientation and equilibrium: What do we need to know about neural control of balance to prevent falls? Age Ageing, 35, ii7–ii11.MathSciNetCrossRefGoogle Scholar
  8. 8.
    Contarino, D., Bertora, G. O., & Bergmann, J. M. (2003). Balance platform: mathematical modeling for clinical evaluation. The International Tinnitus Journal, 9(1), 23–25.Google Scholar
  9. 9.
    Di Berardino, F., Filipponi, E., Barozzi, S., Giordano, G., Alpini, D., & Cesarani, A. (2009). The use of rubber foam pads and “sensory ratios” to reduce variability in static posturography assessment. Gait & Posture, 29(1), 158–160.CrossRefGoogle Scholar
  10. 10.
    Baloh, R. W., Jacobson, K. M., & Beykirch, K. (1998). Static and dynamic posturography in patients with vestibular and cerebellar lesions. Archives of Neurology, 55, 649–654.CrossRefGoogle Scholar
  11. 11.
    Visser, J. E., Carpenter, M. G., van der Kooij, H., & Bloem, B. R. (2008). The clinical utility of posturography. Clinical Neurophysiology, 119(11), 2424–2436.CrossRefGoogle Scholar
  12. 12.
    Shah, V. P., Midha, K. K., & Dighe, S. (1991). Analytical methods evaluation: bioavailability, bioequivalence and pharmacokinetic studies. European Journal of Drug Metabolism and Pharmacokinetics, 16, 249–255.CrossRefGoogle Scholar
  13. 13.
    Egerton, T., Brauer, S. G., & Cresswell, A. G. (2009). Fatigue after physical activity in healthy and balance-impaired elderly. Journal of Aging and Physical Activity, 17, 89–105.CrossRefGoogle Scholar
  14. 14.
    Moe-Nilssen, R., & Helbostad, J. L. (2002). Trunk accelerometry as a measure of balance control during quiet standing. Gait & Posture, 16, 60–68.CrossRefGoogle Scholar
  15. 15.
    Greene, B. R., McGrath, D., Walsh, L., Doheny, E. P., McKeown, D., Garattini, C., et al. (2012). Quantitative falls risk estimation through multi-sensor assessment of standing balance. Physiological Measurement, 33(12), 2049–2063.CrossRefGoogle Scholar
  16. 16.
    Mancini, M., Salarian, A., Carlson-Kuhta, P., et al. (2012). ISway: A sensitive, valid and reliable measure of postural control. Journal of Neuroengineering and Rehabilitation, 9, 59.CrossRefGoogle Scholar
  17. 17.
    Olejnik, S., & Algina, J. (2015). Measures of effect size for comparative studies: appli-cations, interpretations, and limitations. Contemporary Educational Psychology, 25, 241–286.CrossRefGoogle Scholar
  18. 18.
    Gomes, F. P. (2009). Curso de estatística experimental (15th ed., p. 477). Piracicaba: Esalq.Google Scholar
  19. 19.
    Laufer, Y., Barak, Y., & Chemel, I. (2006). Age-related differences in the effect of a perceived threat to stability on postural control. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 61(5), 500–504.CrossRefGoogle Scholar
  20. 20.
    Gill, J., Allum, J. H., Carpenter, M. G., Held-Ziolkowska, M., Adkin, A. L., Honegger, F., et al. (2001). Trunk sway measures of postural stability during clinical balance tests: effects of age. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 56(7), 438–447.CrossRefGoogle Scholar
  21. 21.
    Błaszczyk, J. W., Orawiec, R., Duda-Kłodowska, D., & Opala, G. (2007). Assessment of postural instability in patients with Parkinson’s disease. Experimental Brain Research, 183(1), 107–114.CrossRefGoogle Scholar
  22. 22.
    McGrath, D., Greene, B. R., Sheehan, K., Walsh, L., Kenny, R. A., & Caulfield, B. (2015). Stability of daily home-based measures of postural control over an 8-week period in highly functioning older adults. European Journal of Applied Physiology, 115(2), 437–449.CrossRefGoogle Scholar
  23. 23.
    Fujimoto, C., Murofushi, T., Chihara, Y., Ushio, M., Sugasawa, K., Yamaguchi, T., et al. (2009). Assessment of diagnostic accuracy of foam posturography for peripheral vestibular disorders: analysis of parameters related to visual and somatosensory dependence. Clinical Neurophysiology, 120(7), 1408–1414.CrossRefGoogle Scholar
  24. 24.
    O’Sullivan, M., Blake, C., Cunningham, C., Boyle, G., & Finucane, C. (2009). Correlation of accelerometry with clinical balance tests in older fallers and non-fallers. Age and Ageing, 38(3), 308–313.CrossRefGoogle Scholar

Copyright information

© Korean Society for Precision Engineering 2019

Authors and Affiliations

  • Dongwon Kang
    • 1
  • Jeongwoo Seo
    • 1
  • Junggil Kim
    • 1
  • Jinsoo Lee
    • 1
  • Jinseung Choi
    • 1
    • 2
  • Gyerae Tack
    • 1
    • 2
    Email author
  1. 1.Department of Biomedical EngineeringKonkuk UniversityChungju-siSouth Korea
  2. 2.BK21 Plus Research Institute of Biomedical EngineeringKonkuk University, KoreaChungju-siSouth Korea

Personalised recommendations