Hearing-impaired listeners usually have troubles attending target talker in multi-talker scenes, even with hearing aids (HAs). The problem can be solved with 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 of saccade and head rotation. However, existing methods of eye-gaze estimation did not work reliably, since the listener's strategy of eye-gaze varies and measurements of the two behaviors were not properly combined. Besides, existing methods were based on hand-craft features, which could overlook some important information. In this paper, a head-fixed and a head-free experiments were conducted. We used horizontal electrooculography (HEOG) and neck electromyography (NEMG), which separately measured saccade and head rotation to commonly estimate eye-gaze. Besides traditional classifier and hand-craft features, deep neural networks (DNN) were introduced to automatically extract features from intact waveforms. Evaluation results showed that when the input was HEOG with inertial measurement unit, the best performance of our proposed DNN classifiers achieved 93.3%; and when HEOG was with NEMG together, the accuracy reached 72.6%, higher than that with HEOG (about 71.0%) or NEMG (about 35.7%) alone. These results indicated the feasibility to estimate eye-gaze with HEOG and NEMG.

中文翻译：

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使用深度神经网络的HEOG和颈部EMG的视线估计

听力受损的听众即使在使用助听器（HAs）的情况下，通常也很难在多说话者场景中陪伴目标说话者。该问题可以通过视线转向HA来解决，该方法要求听众目光对准目标。在头部旋转的情况下，视线会受到扫视和头部旋转的影响。但是，现有的眼神估计方法无法可靠地工作，因为听众的眼神策略会发生变化，并且两种行为的测量结果无法正确组合。此外，现有方法基于手工功能，可能会忽略一些重要信息。在本文中，进行了头固定和无头实验。我们使用了水平眼电图（HEOG）和颈部肌电图（NEMG），分别测量扫视和头部旋转以通常估计视线。除了传统的分类器和手工特征外，还引入了深度神经网络（DNN），可以从完整的波形中自动提取特征。评估结果表明，当输入为带有惯性测量单元的HEOG时，我们提出的DNN分类器的最佳性能达到了93.3％；当HEOG与NEMG一起使用时，准确度达到72.6％，高于单独使用HEOG（约71.0％）或NEMG（约35.7％）的准确性。这些结果表明用HEOG和NEMG估计眼睛注视的可行性。评估结果表明，当输入为带有惯性测量单元的HEOG时，我们提出的DNN分类器的最佳性能达到了93.3％；当HEOG与NEMG一起使用时，准确度达到72.6％，高于单独使用HEOG（约71.0％）或NEMG（约35.7％）的准确性。这些结果表明用HEOG和NEMG估计眼睛注视的可行性。评估结果表明，当输入为带有惯性测量单元的HEOG时，我们提出的DNN分类器的最佳性能达到了93.3％；当HEOG与NEMG一起使用时，准确度达到72.6％，高于单独使用HEOG（约71.0％）或NEMG（约35.7％）的准确性。这些结果表明用HEOG和NEMG估计眼睛注视的可行性。