This paper presents an accurate, robust, wearable
measurement system for foot clearance estimation along with
algorithms to provide a real-time estimate of foot height and
orientation. Different configurations of infrared distance meter
sensors were used, both alone and in combination with an inertial
measurement unit. In order to accurately estimate the foot clear-
ance when in presence of daylight and when the foot orientation
changes dynamically during walking, several algorithms were
designed based on physics of sensors and tuned using the acquired
data against a reference system. These algorithms, specific to the
number of sensors, include the estimators of the foot orientation
and estimators of the foot clearance. These estimators are tested
on normal walking (rms error
≤
8.4 mm) and walking with exag-
gerated step heights and inversion-eversion rotations. A Bayesian
fusion of estimators was also implemented to better cope with
the extreme and abnormal walking kinematics while maintaining
a high performance for normal walking. All estimators were
trained on uniformly distributed bootstrapped sub-samples of
data and tested on several normal and abnormal walking data.
The results proved the robustness of the proposed system against
variations in the gait kinematics (
|
mean
|±
standard deviation of
error for heel and toe clearance was equal to or smaller than
3.1
±
9.3 mm when using a Bayesian fusion of three different
estimators) and environment lighting (with an introduced error
of 1 to 4% of actual distance).