Elsevier

Gait & Posture

Volume 30, Issue 3, October 2009, Pages 265-269
Gait & Posture

Runner Up of the ESMAC 2008 Best Paper Award
The Gait Profile Score and Movement Analysis Profile

This paper was selected by an ESMAC Reading Committee headed by Professor Maria Grazia Benedetti. The present paper was edited by Dr. Tim Theologis.
https://doi.org/10.1016/j.gaitpost.2009.05.020Get rights and content

Abstract

The Gait Deviation Index (GDI) has been proposed as an index of overall gait pathology. This study proposes an interpretation of the difference measure upon which the GDI is based, which naturally leads to the definition of a similar index, the Gait Profile Score (GPS). The GPS can be calculated independently of the feature analysis upon which the GDI is based. Understanding what the underlying difference measure represents also suggests that reporting a raw score, as the GPS does, may have advantages over the logarithmic transformation and z-scaling incorporated in the GDI. It also leads to the concept of a Movement Analysis Profile (MAP) to summarise much of the information contained within kinematic data.

A validation study on all children attending a paediatric gait analysis service over 3 years (407 children) provides evidence to support the use of the GPS through analysis of its frequency distribution across different Gross Motor Function Classification System (GMFCS) and Gillette Functional Assessment Questionnaire (FAQ) categories, investigation of intra-session variability, and correlation with the square root of GGI. Correlation with GDI confirms the strong relationship between the two measures.

The study concludes that GDI and GPS are alternative and closely related measures. The GDI has prior art and is particularly useful in applications arising out of feature analysis such as cluster analysis or subject matching. The GPS will be easier to calculate for new models where a large reference dataset is not available and in association with applications using the MAP.

Introduction

Instrumented three-dimensional gait analysis generates kinematic measurements of a wide range of variables across the gait cycle. These span different joints and different planes. Clinical decisions are generally based on an interpretation of the complex information contained in these highly interdependent data. It can often be useful, however, to have a single measure of the ‘quality’ of a particular gait pattern. Such a measure can quantify the overall severity of a condition affecting walking, monitor progress, or evaluate the outcome of an intervention prescribed to improve the gait pattern.

Although other measures have been proposed, the only one to have widespread clinical acceptance is the Gillette Gait Index [1] (GGI, originally referred to as the Normalcy Index), which quantifies the difference between data from one gait cycle for a particular individual and the average of a reference dataset from people exhibiting no gait pathology. The GGI, however, has several shortcomings. These have been well documented and largely overcome in a recent paper proposing an alternative, the Gait Deviation Index [2] (GDI). The GGI incorporates temporal spatial as well as kinematic parameters. The GDI uses only kinematic variables, and might thus be taken as a cleaner reflection of gait quality. The entire variability in kinematic variables across the gait cycle is used, rather than a small number of discrete parameters, thereby removing much of the subjectivity in choosing those parameters. Selection of the parameters for the GGI was specific to children with cerebral palsy whereas the GDI would appear to be a more general measure of gait pathology.

It has been shown that the GGI requires a reasonably large number of people in the reference dataset [3], and that values can vary significantly between different reference datasets [4]. In contrast, values of the GDI appear much less sensitive to differences in the reference data [5]. The GDI proceeds naturally from the gait feature analysis, which provides considerable data compression and provides a framework for other analytical techniques such as cluster analysis for gait classification [6]. Finally, the GDI has been demonstrated to correlate well with GGI and the Functional Assessment Questionnaire (FAQ) in a comprehensive validation study [2].

As with any other measure, the GDI does have some limitations. The technique depends on the preliminary analysis of a large dataset containing examples of all likely gait deviations (3351 subjects were used in the original study [2]). Although the authors have made the gait features derived from this analysis available for use, this does limit the potential for this technique to be expanded to other applications. Deriving a similar index for a new biomechanical model based on a different marker set, incorporating functional calibration, or including more complex modelling of the foot, for example, would be a considerable undertaking.

The GDI is a scaled version of the Euclidean distance of a subject's kinematics from the average of a reference dataset calculated in a basis comprised of 15 gait features. At first sight this appears a somewhat abstract quantity and the clinical interpretation of the measure is based upon its scaling relative to the reference dataset. That scaling has been chosen to ensure a measure with good statistical properties.

This paper proposes a simpler interpretation of the distance measure underlying the GDI, which leads to the proposal of a modified measure that can be calculated independently of the feature analysis. This adds to our understanding of how it can be interpreted clinically, and suggests that there may be advantages in using a raw score (as opposed to a scaled index). The paper thus presents data to validate such a raw score, and uses the new understanding of the distance measure as a basis for considering the relative advantages and disadvantages of raw scores or scaled indices.

Section snippets

Interpreting the difference measure of the GDI

The key to understand the difference measure used in the GDI is to recognise that the feature analysis is based on projecting the original gait data onto the gait features using an orthonormal transformation. By definition, the Euclidean distance – and therefore the RMS difference – between any two gait vectors will be preserved by any such transformation. Thus, if all 459 gait features were used in the GDI (rather than just the first 15), the difference measure used in the GDI would be the RMS

Results

Data from the 407 children were used. 271 had cerebral palsy, 88 had general orthopaedic conditions (such as Perthes disease, slipped upper femoral epiphysis and rotational malalignment), 43 had other neurological conditions (such as spina bifida, hereditary spastic paraplegia and acquired brain injuries) and five were idiopathic toe walkers (Table 1).

The frequency distributions of the GPS for the categories of the FAQ (levels 6–10) and GMFCS (I–III) and also for the children with no gait

Discussion

The GPS has strong face validity being based on the RMS difference between gait data for an individual child and the average data from children with no gait pathology. Analysis of intra-session variability suggests that it is also a reliable measure (within a single session). The moderate correlation with GGI and the significant differences in GPS between both FAQ and GMFCS levels provide further evidence of validity.

The extremely strong correlation between the GPS and the GDI confirms the

Conclusion

The study concludes that GDI and GPS are alternative and closely related measures. The GDI has prior art and is particularly useful in applications arising out of feature analysis such as cluster analysis or subject matching. The GPS will be easier to calculate for new models where a large reference dataset is not available and in association with applications using the MAP.

Conflict of interest

Authors Richard Baker, Jennifer L. McGinley, and Oren Tirosh have filed a patent through their employers for an invention making use of some of the ideas described in this paper. Otherwise none of the authors have any financial and personal relationships with other people or organisations that could inappropriately influence (bias) their work.

Acknowledgements

The authors acknowledge funding support from the National Health and Medical Research Council of Australia [Centre for Clinical Research Excellence in Gait Analysis and Gait Rehabilitation Grant No 264597].

References (8)

There are more references available in the full text version of this article.

Cited by (465)

View all citing articles on Scopus
View full text