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The quantification of gait stability can provide valuable
information when evaluating subjects for age related and
neuromuscular disease changes. Using tri-axial inertial
measurement units (IMU) for acceleration and rotational
data provide a non-linear profile for this type of
movement. As subjects traverse various surfaces
representing decreasing stability, the different phasing of
gait data make comparisons difficult. By converting from
time to frequency domain data, the phase effects can be
ignored, allowing for significant correlations. In this
study, 12 subjects provided gait information over various
surfaces while wearing an IMU. Instabilities were
determined by comparing frequency domain data over
less stable surfaces to frequency domain data of neural
network (NN) models representing the normal gait for
any given participant. Time dependent data from 2 axes
of acceleration and 2 axes of rotation were converted
using a discrete Fourier transform (FFT) algorithm. The
data over less stable surfaces were compared to the
normal gait NN model by averaging the Pearson product
moment correlation (r) values. This provided a method
to quantify the decreased stability. Data showed
progressively decreasing correlation coefficient values as
subjects encountered progressively less stable surface
environments. This methodology has allowed for the
quantification of instability in gait situations for
application in real-time fall prevention situations.
