English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

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