Gaze-contingent decoding of human navigation intention on an autonomous wheelchair platform
Author(s)
Subramanian, Mahendran
Park, Suhyung
Orlov, Pavel
Shafti, Ali
Faisal, Aldo
Type
Conference Paper
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).
Date Acceptance
2021-02-01
Citation
10th International IEEE EMBS Conference on Neural Engineering
Publisher
IEEE
Journal / Book Title
10th International IEEE EMBS Conference on Neural Engineering
Copyright Statement
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Identifier
https://ieeexplore.ieee.org/document/9441218
Source
10th International IEEE EMBS Conference on Neural Engineering
Subjects
Science & Technology
Technology
Life Sciences & Biomedicine
Computer Science, Theory & Methods
Engineering, Biomedical
Neurosciences
Computer Science
Engineering
Neurosciences & Neurology
SYSTEM
Publication Status
Published
Start Date
2021-05-04
Finish Date
2021-05-06
Coverage Spatial
Virtual