The Gait Profile Score and Movement Analysis Profile

Murdoch Childrens Research Institute, Royal Children's Hospital, Department of Mechanical and Manufacturing Engineering, The University of Melbourne, Australia.
Gait & posture (Impact Factor: 2.75). 08/2009; 30(3):265-9. DOI: 10.1016/j.gaitpost.2009.05.020
Source: PubMed


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.

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Available from: Michael H Schwartz, Nov 18, 2014
    • "When a direct comparison of timeseries data is considered, variability in lengths of the timeseries need to be addressed. The timeseries are either interpolated to a common fixed length (51 samples) [4], [5] or the Fourier Transform is applied [25]. Gait indices can be directly calculated from such a compact representation without the need to extract a set of suitable gait variables. "

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    • "ained from the different types of regressors ( PROM , muscle strength , and spasticity ) . Moreover , the Pearson correlations were calculated to evaluate the associations between the RMSE_Model value of each observation for each joint ( i . e . , hip , knee , and ankle ) and the normalized velocity ( Baker 2013 ) and Gait profile Scores ( GPS ) ( Baker et al . 2009 ) . The effect of the gait deviations on the quality of the recovered curves can be estimated using these associations . The statistical analysis was performed using STATISTICA 10 ( StatSoft , USA ) . A statistical significance of p # 0 . 05 was adopted for the tests . Finally , the absolute value of the model error e p ( AVEp ) was cal"
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    • "The study included 16 children aged 7 to 12 years old. Clinical indices based on kinematic data (the Gillette Gait Index (Schutte et al., 2000)), the Gait Deviation Index (Schwartz and Rozumalski, 2008) and the Gait Profile Score (Baker et al., 2009) and on dynamic data (Rozumalski and Schwartz, 2011) have been proposed. Age and speed variations were not considered in their calculations, and there are difficulties in comparing healthy and pathological gaits in children because of walking speed differences. "
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