  A semi-autonomous prosthesis control based on computer vision can be used to
improve performance while decreasing the cognitive burden, especially when
using advanced systems with multiple functions. However, a drawback of this
approach is that it relies on the complex processing of a significant amount of
data (e.g., a point cloud provided by a depth sensor), which can be a challenge
when deploying such a system onto an embedded prosthesis controller. In the
present study, therefore, we propose a novel method to reconstruct the shape of
the target object using minimal data. Specifically, four concurrent laser
scanner lines provide partial contours of the object cross-section. Simple
geometry is then used to reconstruct the dimensions and orientation of
spherical, cylindrical and cuboid objects. The prototype system was implemented
using depth sensor to simulate the scan lines and vibrotactile feedback to aid
the user during aiming of the laser towards the target object. The prototype
was tested on ten able-bodied volunteers who used the semi-autonomous
prosthesis to grasp a set of ten objects of different shape, size and
orientation. The novel prototype was compared against the benchmark system,
which used the full depth data. The results showed that novel system could be
used to successfully handle all the objects, and that the performance improved
with training, although it was still somewhat worse compared to the benchmark.
The present study is therefore an important step towards building a compact
system for embedded depth sensing specialized for prosthesis grasping.
