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

Hearing-impaired listeners usually have troubles attending target talker inmulti-talker scenes, even with hearing aids (HAs). The problem can be solvedwith eye-gaze steering HAs, which requires listeners eye-gazing on the target.In a situation where head rotates, eye-gaze is subject to both behaviors ofsaccade and head rotation. However, existing methods of eye-gaze estimation didnot work reliably, since the listener's strategy of eye-gaze varies andmeasurements of the two behaviors were not properly combined. Besides, existingmethods were based on hand-craft features, which could overlook some importantinformation. In this paper, a head-fixed and a head-free experiments wereconducted. We used horizontal electrooculography (HEOG) and neckelectromyography (NEMG), which separately measured saccade and head rotation tocommonly estimate eye-gaze. Besides traditional classifier and hand-craftfeatures, deep neural networks (DNN) were introduced to automatically extractfeatures from intact waveforms. Evaluation results showed that when the inputwas HEOG with inertial measurement unit, the best performance of our proposedDNN classifiers achieved 93.3%; and when HEOG was with NEMG together, theaccuracy reached 72.6%, higher than that with HEOG (about 71.0%) or NEMG (about35.7%) alone. These results indicated the feasibility to estimate eye-gaze withHEOG and NEMG.

Eye-gaze Estimation with HEOG and Neck EMG using Deep Neural Networks