  We have pioneered the Where-You-Look-Is Where-You-Go approach to controlling
mobility platforms by decoding how the user looks at the environment to
understand where they want to navigate their mobility device. However, many
natural eye-movements are not relevant for action intention decoding, only some
are, which places a challenge on decoding, the so-called Midas Touch Problem.
Here, we present a new solution, consisting of 1. deep computer vision to
understand what object a user is looking at in their field of view, with 2. an
analysis of where on the object's bounding box the user is looking, to 3. use a
simple machine learning classifier to determine whether the overt visual
attention on the object is predictive of a navigation intention to that object.
Our decoding system ultimately determines whether the user wants to drive to
e.g., a door or just looks at it. Crucially, we find that when users look at an
object and imagine they were moving towards it, the resulting eye-movements
from this motor imagery (akin to neural interfaces) remain decodable. Once a
driving intention and thus also the location is detected our system instructs
our autonomous wheelchair platform, the A.Eye-Drive, to navigate to the desired
object while avoiding static and moving obstacles. Thus, for navigation
purposes, we have realised a cognitive-level human interface, as it requires
the user only to cognitively interact with the desired goal, not to
continuously steer their wheelchair to the target (low-level human
interfacing).
