Posted by: Adam Graf
« on: August 15, 2016, 10:37:21 AM »The GDI-Kinetic: A new index for quantifying kinetic deviations from normal gait
Adam Rozumalski a,b,*, Michael H. Schwartz a,b,c
A B S T R A C T
This article introduces a new index, the GDI-Kinetic; a direct analog of the GDI based on joint kinetics
rather than kinematics. The method consists of: (1) identifying ‘‘features’’ of the raw gait kinetic data
using singular value decomposition, (2) identifying a subset of features that account for a large
percentage of the information in the raw gait kinetic data, (3) expressing the raw data from a group of
typically developing children as a linear combination of these features, (4) expressing a subject’s raw
data as a linear combination of these features, (5) calculating the magnitude of the difference between
the subject and the mean of the control, and (6) scaling and transforming the difference, in order to
provide a simple, and statistically well-behaved, measure. Linear combinations of the first 20 gait
features produced a 91% faithful reconstruction of the data. Concurrent and face validity for the GDIKinetic
are presented through comparisons with the GDI, Gillette Functional Assessment Questionnaire
Walking Scale (FAQ), and topographic classifications within the diagnosis of Cerebral Palsy (CP). The GDIKinetic
and GDI are linearly related but not strongly correlated (r2 = 0.24). Like the GDI, the GDI-Kinetic
scales with FAQ level, distinguishes levels from one another, and is normally distributed across FAQ
levels six to ten, and among typically developing children. The GDI-Kinetic also scales with respect to
clinical involvement based on topographic CP classification in Hemiplegia types I–IV, Diplegia, Triplegia,
and Quadriplegia. The GDI-Kinetic complements the GDI in order to give a more comprehensive measure
of gait pathology.
Adam Rozumalski a,b,*, Michael H. Schwartz a,b,c
A B S T R A C T
This article introduces a new index, the GDI-Kinetic; a direct analog of the GDI based on joint kinetics
rather than kinematics. The method consists of: (1) identifying ‘‘features’’ of the raw gait kinetic data
using singular value decomposition, (2) identifying a subset of features that account for a large
percentage of the information in the raw gait kinetic data, (3) expressing the raw data from a group of
typically developing children as a linear combination of these features, (4) expressing a subject’s raw
data as a linear combination of these features, (5) calculating the magnitude of the difference between
the subject and the mean of the control, and (6) scaling and transforming the difference, in order to
provide a simple, and statistically well-behaved, measure. Linear combinations of the first 20 gait
features produced a 91% faithful reconstruction of the data. Concurrent and face validity for the GDIKinetic
are presented through comparisons with the GDI, Gillette Functional Assessment Questionnaire
Walking Scale (FAQ), and topographic classifications within the diagnosis of Cerebral Palsy (CP). The GDIKinetic
and GDI are linearly related but not strongly correlated (r2 = 0.24). Like the GDI, the GDI-Kinetic
scales with FAQ level, distinguishes levels from one another, and is normally distributed across FAQ
levels six to ten, and among typically developing children. The GDI-Kinetic also scales with respect to
clinical involvement based on topographic CP classification in Hemiplegia types I–IV, Diplegia, Triplegia,
and Quadriplegia. The GDI-Kinetic complements the GDI in order to give a more comprehensive measure
of gait pathology.