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
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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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    • "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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    Clinical biomechanics (Bristol, Avon) 04/2015; 30(6). DOI:10.1016/j.clinbiomech.2015.03.027 · 1.97 Impact Factor
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    • "Key temporal–spatial and kinematic variables for the trunk and lower limb were analysed (Table 2). The Gait Profile Score (GPS) was calculated as a summary of lower limb kinematics [15]. To avoid erroneously doubling the sample size, data were analysed for one limb only, namely the involved limb of children with hemiplegia, and the left lower limb (selected randomly by coin toss) for TD and children with diplegia. "
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    ABSTRACT: Independently ambulant children with Cerebral Palsy (CP) often report balance difficulties when walking in challenging settings. The aim of this study was to compare gait in children with CP to typically developing (TD) children walking over level ground and uneven ground, as an evaluation of dynamic balance. Thirty-four children participated, 17 with CP (10 hemiplegia and 7 diplegia, mean age 10 years) and 17 TD (mean age 10 years 1 month). Three-dimensional kinematic and kinetic data of the lower limbs and trunk were captured during walking over level and uneven ground using Codamotion(®). Statistical analysis was performed using a mixed-effects model two-factor Analysis of Variance (Group×Surface). Over both surfaces, children with CP showed increased trunk movement in the sagittal (Group effect, p<0.001) and transverse planes (p<0.001), and increased pelvic movement in the coronal plane (p=0.008), indicating impaired trunk control. Peak separation between the centre of mass and centre of pressure was reduced in CP, indicating impaired dynamic balance (p=0.027). TD children made a number of significant adaptations to uneven ground, including reduced hip extension (mean difference 3.4°, 95% CI [-5.3, -1.0] p=0.006), and reduced ankle movement in the sagittal (5.2°, 95% CI [0.01, 10] p=0.049) and coronal planes (2.4°, 95% CI [0.3, 4.5], p=0.029), but these adaptations were not measured in CP. A significant Group×Surface interaction was detected for knee sagittal range (p=0.009). The findings indicate that children with CP walk show impaired control of trunk movement and are less able to adapt their gait to uneven ground, particularly at the ankle. Copyright © 2015 Elsevier B.V. All rights reserved.
    Gait & Posture 02/2015; 41(2). DOI:10.1016/j.gaitpost.2015.02.001 · 2.75 Impact Factor
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