Abstract
The ankle foot orthotic (AFO) has been around for centuries. They were created to
augment functionality of an ankle damaged due to injury or disease. A common reason a
patient might be prescribed an AFO is a condition called foot drop. Foot drop can be caused
by many conditions, but the most common reason is a stroke. Foot drop can be
characterized by the inability to raise and/or lower a patient’s foot. This incapacitation of
the patient’s foot leads to unnatural gaits and joint fatigue, as well as increasing the
patient’s likelihood of tripping and becoming seriously injured. Hard plastic AFOs that hold
a patient’s foot in a neutral position are the current standard for combating foot drop.
These AFOs come in many different shapes and sizes, which emphasizes the wide variety in
functionality of someone with foot drop. Unfortunately, the restrictive nature of the AFO
can cause unnatural movements in the patient’s foot; these unnatural tendencies are more
exaggerated when walking down stairs and ramps, as the natural gait is to land toe first, the
opposite of what the brace allows the patient to do. The purpose of this project is to create
a sensor system for an AFO to help identify varying terrain. In the future this information
can then be made to control an active AFO.
Each terrain type will be first measured by a pair of simple infrared range finder,
attached on the lower leg, one range finder looks ahead of the user and the other looks
straight down at the ground. Models for the ground conditions can be established by
representing each with Fourier series created using RANdom Sample Consensus (RANSAC).
RANSAC coefficients will be scaled off the rate of data coming in and gait speed. Each model
has a period term so the data can easily be scaled to match the pattern of walking
regardless of pace. Gait speed will be measured using the downward facing ankle-mounted
rangefinder, but with a threshold to determine when the foot is in contact with the ground.
Once this initial set-up is completed, the system can take in data live and provide a
prediction of the type of ground the patient is walking over, using pattern recognition
techniques. The hope for this project is that if the system can accurately predict the change
in ground type from, for example, level walking to walking down a ramp, an AFO could then
be made to adjust itself, giving the patient a more natural gait, even when encountering
adverse conditions. A byproduct of constantly using a patient’s own gait to measure ground
type is the ability to track a patient’s changing gait over time, giving therapists a valuable
new tool for tracking progress in a patient.