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.