COMPARISON BETWEEN MODEL AND EXPERIMENTAL ORBITAL STABILITY ANALYSIS OF GAIT

Federico Riva (1), Kristina Mayberry (1), Rita Stagni (1)

1. DEIS, Department of Electronics, Computer Sciences and Systems, University of

Bologna, Italy

Introduction

Falls in the elderly represent a major community

and public health problem [Heinrich, 2010].

Stability of locomotion is one of the more

important factors for the clinicians to look for

during assessment procedure [Hurmuzlu, 1994].

Many stability indices have been proposed for

clinical application; some authors applied orbital

stability analysis (via Maximum Floquet

multipliers, maxFM) to biomechanics with

promising results [Dingwell, 2007], but still the use

of this technique in the assessment of fall risk has

been deemed controversial [Hamacher, 2011]. The

possibility to obtain reliable orbital stability

measures from a light portable device such as a

single inertial sensor could fasten the acquisition

procedure, but still it is not clear how experimental

characteristics affect the results. Simulations

represent a powerful tool to test reliability of

results. The aim of this study was to compare

orbital stability results coming from acceleration

data of a stable walking model to experimental

results obtained with the same implementation.

Methods

A 2-dimensional, 5-link stable biped walking model

was implemented [Solomon, 2010]. Orbital stability

analysis was performed on a 2-dimensional state

space, composed by vertical (VT) and anterior-

posterior (AP) accelerations of the trunk at the level

of L5. Signals affected by simulated experimental

noise were also analysed. Orbital stability analysis

of experimental acceleration data coming from 12

healthy subjects performing 1 minute walking trials

(about 40 step cycles) at their preferred speed with

an inertial sensor (Dynaport, McRoberts) placed at

the level of L5 was calculated, based on the same

state space used for the model analysis (VT and AP

accelerations). Mean values of maxFMs across the

gait cycle were calculated on increasing number of

steps (from 3 to 300) for both state spaces.

Results

MaxFM calculated on the non-noisy and noisy

accelerations state space showed basically the same

results. The value of the maxFM varied with

respect of the number of gait cycles. For less than

30 cycles, values of maxFM gradually decrease,

starting from very high values; from 40 cycles on,

values of maxFM stabilize around the value 0.34.

MaxFMs calculated on experimental accelerations

state spaces showed decreasing value for increasing

number of cycles, reaching 0.3 for 40 cycles.

Figure 1: a- MaxFMs and their Standard Deviation

(SD) calculated on state spaces composed by noisy

acceleration signals coming from the model. b-

MaxFMs and their SD calculated on state spaces

composed by experimental acceleration data.

Discussion

Experimental results seem to confirm the values

obtained from the model for maxFMs calculated

upon accelerations state spaces. For an appropriate

number of cycles (at least 40), maxFMs confirm

that the gait is stable. For less than 40 gait cycles,

values of maxFMs are not believed to be reliable,

both for model and experimental analysis. Further

studies are needed to understand if longer

experimental walking trials (longer than 40 gait

cycles) lead to the same results.

Acknowledgements

The authors gratefully thank Dr. Martijn Wisse for

References

Dingwell et al, J Biomech Eng 129(4):586-593,

2007.

Hamache et al, J R Soc Interface 8(65):1682-1698,

2011.

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his contribution in the implementation of the model.

Presentation 1181 − Topic 20. Gait and posture S227

ESB2012: 18th Congress of the European Society of Biomechanics Journal of Biomechanics 45(S1)