  Lower limb prosthesis can benefit from embedded systems capable of applying
computer vision techniques to enhance autonomous control and context awareness
for intelligent decision making. In order to fill in the gap of current
literature of autonomous systems for prosthetic legs employing computer vision
methods, we evaluate the performance capabilities of two off-the-shelf
platforms, the Jetson TX2 and Raspberry Pi 3, by assessing their CPU load,
memory usage, run time and classification accuracy for different image sizes
and widespread computer vision algorithms. We make use of a dataset that we
collected for terrain recognition using images from a camera mounted on a leg,
which would enable context-awareness for lower limb prosthesis. We show that,
given reasonably large images and an appropriate frame selection method, it is
possible to identify the terrain that a subject is going to step on, with the
possibility of reconstructing the surface and obtaining its inclination. This
is part of a proposed system equipped with an embedded camera and inertial
measurement unit to recognize different types of terrain and estimate the slope
of the surface in front of the subject.
