Gaze-contingent decoding of human navigation intention on an autonomous wheelchair platform

TL;DR

This paper introduces a gaze-contingent decoding system for autonomous wheelchairs that interprets visual attention and motor imagery to understand user navigation intentions, enabling goal-directed control without continuous steering.

Contribution

It presents a novel combination of deep vision, attention analysis, and machine learning to decode navigation intentions from eye movements and motor imagery for autonomous wheelchair control.

Findings

Decodable eye-movements during motor imagery indicate intention.

System successfully identifies user goals like doors or objects.

Autonomous navigation to selected targets achieved with obstacle avoidance.

Abstract

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).