Gait Ratios and Variability Indices to Quantify the Effect of Using Smartphones in Dual-Task Walking

  • Carlotta Caramia
  • Ivan Bernabucci
  • Carmen D’Anna
  • Cristiano De Marchis
  • Maurizio Schmid
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 68/2)


Smartphone use is one of the most common activities performed while walking: recent studies showed how this behaviour affected spatio-temporal, smoothness, symmetry and regularity gait parameters. In this study, we investigated a subset of additional gait parameters, potentially indicative of gait instability, to check whether concurrent smartphone activities cause deviations from stable walking. Ten young healthy adults were asked to walk outdoor normally and while performing five smartphone-based dual-task activities, with different levels of cognitive effort. Three groups of gait parameters, extracted by a single waist-mounted tri-axial inertial sensor, were analyzed: Gait Ratios group included Stride-to-Stance Time Ratio (SSTR)—equal to the golden ratio \( \upvarphi \)  ≈ 1.618 in normal walking—and Walk Ratio (WR)—the ratio between Step Length (SL) and cadence, roughly constant within healthy subjects—Variability Measures group included Coefficients of Variation (CV) of SL and step time; Acceleration Ratios group composed of Root Mean Squared acceleration Ratios (RMSR)—the ratio between rms along a single direction and the total rms acceleration. When a dual-task is present, SSTR did not show significant variations from Baseline. A continuous typing activity with low cognitive engagement caused a significant decrease of WR with respect to all the other tasks. RMSR in the mediolateral direction and the CV SL showed visible yet not significant proportion with the amount of experienced cognitive effort. The resulting alterations were in general inconclusive as to their possible link with a reduced ability to adapt the locomotion structure to the context changes, even if for some parameters the observed proportion with cognitive effort and visual domain may need to be deepened on a bigger sample size, possibly including more challenging dual-task demands.


Gait analysis Inertial sensors Smartphone use 


Conflict of Interest

The authors declare that they have no conflict of interest.


  1. 1.
    Urbanek, J.K., Zipunnikov, V., Harris, T., et al.: Validation of gait characteristics extracted from raw accelerometry during walking against measures of physical function, mobility, fatigability, and fitness. The Journals of Gerontology, (2016).
  2. 2.
    Hausdorff, H.M.: Gait variability: methods, modeling and meaning. Journal of neuroengineering and rehabilitation 2(1), 19 (2005).Google Scholar
  3. 3.
    Sekiya, N., Nagasaki, H., Ito, H., et al.: The invariant relationship between step length and step rate during free walking. Journal of Human Movement Studies 30(6), 241–257 (1996).Google Scholar
  4. 4.
    Sekiya, N., Nagasaki, H.: Reproducibility of the walking patterns of normal young adults: test-retest reliability of the walk ratio (step-length/step-rate). Gait & posture 7(3), 225–227 (1998).Google Scholar
  5. 5.
    Rota, V., Perucca, L., Simone, A., et al.: Walk ratio (step length/cadence) as a summary index of neuromotor control of gait: application to multiple sclerosis. International Journal of Rehabilitation Research 34(3), 265–269 (2011).Google Scholar
  6. 6.
    Iosa, M., Fusco, A., Marchetti, F., et al.: The Golden Ratio of Gait Harmony: Repetitive Proportions of Repetitive Gait Phases. BioMed Research International 2013 (2013).Google Scholar
  7. 7.
    Iosa, M., Bini, F., Marinozzi, F., et al.: Stability and Harmony of Gait in Patients with Subacute Stroke. Journal of medical and biological engineering 36(5), 635–643 (2016).Google Scholar
  8. 8.
    Serrao, M., Chini, G., Iosa, M., et al.: Harmony as a convergence attractor that minimizes the energy expenditure and variability in physiological gait and the loss of harmony in cerebellar ataxia. Clinical Biomechanics 48, 15–23 (2017).Google Scholar
  9. 9.
    Sekine, M., Tamura, T., Yoshida, Y., et al.: A gait abnormality measure based on root mean square of trunk acceleration. Journal of neuroengineering and rehabilitation 10(1), 118 (2013).Google Scholar
  10. 10.
    Matsushima, A., Yoshida, K., Genno, H., et al.: Clinical assessment of standing and gait in ataxic patients using a triaxial accelerometer. Cerebellum & ataxias 2(1), 9 (2015).Google Scholar
  11. 11.
    Patel, P., Lamar, M., Bhatt, T.: Effect of type of cognitive task and walking speed on cognitive-motor interference during dual-task walking. Neuroscience 260, 140–148 (2014).Google Scholar
  12. 12.
    Lamberg, E.M., Muratori, L.M.: Cell phones change the way we walk. Gait & posture 35(4), 688–690 (2012).Google Scholar
  13. 13.
    Schabrun, S.M., van de Hoorn, W., Moorcraft, A., et al.: Texting and Walking: Strategies for Postural Control and Implications for Safety. PLoS One (2014).
  14. 14.
    Caramia, C., Bernabucci, I., D’Anna, C., et al.: Gait parameters are differently affected by concurrent smartphone-based activities with scaled levels of cognitive effort. PLoS One (2017).
  15. 15.
    Berolo, S., Steenstra, I., Amick, B.C. III, et al.: A comparison of two methods to assess the usage of mobile hand-held communication devices. Journal of Occupational and Environmental Hygiene 12(3), 276–285 (2015).Google Scholar
  16. 16.
    Haga, S., Sano, A., Sekine, Y., et al.: Effect of using a smart phone on pedestrians’ attention and walking. Procedia Manufacturing 3, 2574–2580 (2015).Google Scholar
  17. 17.
    Caramia, C., Bernabucci, I., Conforto, S., et al.: Spatio-temporal gait parameters as estimated from wearable sensors placed at different waist level. In: Biomedical Engineering and Sciences (IECBES), 2016 IEEE EMBS Conference on, pp. 727–730, Malaysia (2016).Google Scholar
  18. 18.
    McCamley, J., Donati, M., Grimpampi, E., et al.: An enhanced estimate of initial contact and final contact instants of time using lower trunk inertial sensor data. Gait & posture 36, 316–318 (2012).Google Scholar
  19. 19.
    Zijlstra, W., Hof, A.L.: Assessment of spatio-temporal gait parameters from trunk accelerations during human walking. Gait & posture 18(2), 1–10 (2003).Google Scholar
  20. 20.
    Gonzales, R.C., Alvarez, D., Lopez, A.M.: Modified Pendulum Model for mean Step Length Estimation. In: 29th Annual International Conference of the IEEE, EMBS 2007, pp. 1371–1374, Lyon, France (2007).Google Scholar
  21. 21.
    Kalron, A: Construct validity of the walk ratio as a measure control of gait control in people with multiple sclerosis without mobility aids. Gait & posture 47, 103–107 (2016).Google Scholar
  22. 22.
    Nagasaki, H., Itoh, K., Hashizume, K., et al.: Walking patterns and finger rhythm of older adults. Perceptual and motor skills 82(2), 435–447 (1996).Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Carlotta Caramia
    • 1
  • Ivan Bernabucci
    • 1
  • Carmen D’Anna
    • 1
  • Cristiano De Marchis
    • 1
  • Maurizio Schmid
    • 1
  1. 1.Department of EngineeringRoma Tre UniversityRomeItaly

Personalised recommendations