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
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    • "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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    ABSTRACT: The aim of this study was to evaluate whether clinical parameters are sufficient using, a multilinear regression model, to reproduce the sagittal plane joint angles (hip, knee, and ankle) in cerebral palsy gait. A total of 154 patients were included. The two legs were considered (308 observations). Thirty-six clinical parameters were used as regressors (range of motion, muscle strength, and spasticity of the lower). From the clinical gait analysis, the joint angles of the sagittal plane were selected. Results showed that clinical parameter does not provide sufficient information to recover joint angles and/or that the multilinear regression model is not an appropriate solution.
    Computer Methods in Biomechanics and Biomedical Engineering 08/2015; DOI:10.1080/10255842.2015.1064112 · 1.77 Impact Factor
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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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    ABSTRACT: Reference databases are mandatory in orthopaedics because they enable the detection of gait abnormalities in patients. Such databases rarely include data on children under seven years of age. In young children, gait is principally influenced by age and walking speed. The influence of the age-speed interaction has not been well established. Therefore, the objective of the present study is to propose normative values for biomechanical gait parameters in children taking into account age, walking speed, and the age-speed interaction. Gait analyses were performed on 106 healthy children over a large age range (between one and seven years of age) during gait trials at a self-selected speed. From these gait cycles, biomechanical parameters, such as the joint angles and joint power of the lower limbs, were computed. Specific peak values and the times of occurrence of each biomechanical gait parameter were identified. Linear regressions are proposed for studying the influence of age, walking speed and the age-speed interaction. Most of the regressions achieved good accuracy in fitting the curve peaks and times of occurrence, and the normal reference targets of biomechanical parameters could be deduced from these regressions. The biomechanical gait parameters of a pathological case were plotted against the normal reference targets to illustrate the relevance of the proposed targeting method. The normal reference targets for biomechanical gait parameters based on age-speed regressions in a large database might help clinicians detect gait abnormalities in children from one to seven years of age. Copyright © 2015 Elsevier Ltd. All rights reserved.
    Clinical biomechanics (Bristol, Avon) 04/2015; 30(6). DOI:10.1016/j.clinbiomech.2015.03.027 · 1.97 Impact Factor
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