Foot clearance parameters provide useful insight
into tripping risks during walking. This paper proposes a technique
for the estimate of key foot clearance parameters using
inertial sensor (accelerometers and gyroscopes) data. Fifteen
features were extracted from raw inertial sensor measurements,
and a regression model was used to estimate two key foot clearance
parameters: First maximum vertical clearance (mx1) after
toe-off and the Minimum Toe Clearance (MTC) of the swing
foot. Comparisons are made against measurements obtained
using an optoelectronic motion capture system (Optotrak), at 4
different walking speeds. General Regression Neural Networks
(GRNN) were used to estimate the desired parameters from
the sensor features. Eight subjects foot clearance data were
examined and a Leave-one-subject-out (LOSO) method was
used to select the best model. The best average Root Mean
Square Errors (RMSE) across all subjects obtained using all
sensor features at the maximum speed for mx1 was 5.32 mm
and for MTC was 4.04 mm. Further application of a hillclimbing
feature selection technique resulted in 0.54-21.93%
improvement in RMSE and required fewer input features.
The results demonstrated that using raw inertial sensor data
with regression models and feature selection could accurately
estimate key foot clearance parameters.