Comparing In-ear EOG for Eye-Movement Estimation With Eye-Tracking: Accuracy, Calibration, and Speech Comprehension

The Deep-Match Framework: R-Peak Detection in Ear-ECG

The Ear-ECG provides a continuous Lead I like electrocardiogram (ECG) by measuring the potential difference related to heart activity by electrodes which are embedded within earphones. However, the significant increase in wearability and comfort enabled by Ear-ECG is often accompanied by a degradation in signal quality - an obstacle that is shared by the majority of wearable technologies. We aim to resolve this issue by introducing a Deep Matched Filter (Deep-MF) for the highly accurate detection of R-peaks in wearable ECG, thus enhancing the utility of Ear-ECG in real-world scenarios. The Deep-MF consists of an encoder stage, partially initialised with an ECG template, and an R-peak classifier stage. Through its operation as a Matched Filter, the encoder searches for matches with an ECG template in the input signal, prior to filtering these matches with the subsequent convolutional layers and selecting peaks corresponding to the ground-truth ECG. The latent representation of R-peak information is then fed into a R-peak classifier, of which the output provides precise R-peak locations. The proposed Deep Matched Filter is evaluated using leave-one-subject-out cross-validation over 36 subjects with an age range of 18-75, with the Deep-MF outperforming existing algorithms for R-peak detection in noisy ECG. The Deep-MF achieves a median R-peak recall of 94.9% and a median precision of 91.2% across subjects when evaluated with leave-one-subject-out cross validation. Overall, this Deep-Match framework serves as a valuable step forward for the real-world functionality of Ear-ECG and, through its interpretable operation, the acceptance of deep learning models in e-Health.

Practical utility of a head-mounted gaze-directed beamforming system

Assistive auditory devices that enhance signal-to-noise ratio must follow the user's changing attention; errors could lead to the desired source being suppressed as noise. A method for measuring the practical benefit of attention-following speech enhancement is described and used to show a benefit for gaze-directed beamforming over natural binaural hearing. First, participants watched a recorded video conference call between two people with six additional interfering voices in different directions. The directions of the target voices corresponded to the spatial layout of their video streams. A simulated beamformer was yoked to the participant's gaze direction using an eye tracker. For the control condition, all eight voices were spatially distributed in a simulation of unaided binaural hearing. Participants completed questionnaires on the content of the conversation, scoring twice as high in the questionnaires for the beamforming condition. Sentence-by-sentence intelligibility was then measured using new participants who viewed the same audiovisual stimulus for each isolated sentence. Participants recognized twice as many words in the beamforming condition. The results demonstrate the potential practical benefit of gaze-directed beamforming for hearing aids and illustrate how detailed intelligibility data can be retrieved from an experiment that involves behavioral engagement in an ongoing listening task.

Multilevel Modeling of Gaze From Listeners With Hearing Loss Following a Realistic Conversation

PURPOSE

There is a need for tools to study real-world communication abilities in people with hearing loss. We outline a potential method for this that analyzes gaze and use it to answer the question of when and how much listeners with hearing loss look toward a new talker in a conversation.

METHOD

Twenty-two older adults with hearing loss followed a prerecorded two-person audiovisual conversation in the presence of babble noise. We compared their eye-gaze direction to the conversation in two multilevel logistic regression (MLR) analyses. First, we split the conversation into events classified by the number of active talkers within a turn or a transition, and we tested if these predicted the listener's gaze. Second, we mapped the odds that a listener gazed toward a new talker over time during a conversation transition.

RESULTS

We found no evidence that our conversation events predicted changes in the listener's gaze, but the listener's gaze toward the new talker during a silence-transition was predicted by time: The odds of looking at the new talker increased in an s-shaped curve from at least 0.4 s before to 1 s after the onset of the new talker's speech. A comparison of models with different random effects indicated that more variance was explained by differences between individual conversation events than by differences between individual listeners.

CONCLUSIONS

MLR modeling of eye-gaze during talker transitions is a promising approach to study a listener's perception of realistic conversation. Our experience provides insight to guide future research with this method.

Head movement and its relation to hearing

Head position at any point in time plays a fundamental role in shaping the auditory information that reaches a listener, information that continuously changes as the head moves and reorients to different listening situations. The connection between hearing science and the kinesthetics of head movement has gained interest due to technological advances that have increased the feasibility of providing behavioral and biological feedback to assistive listening devices that can interpret movement patterns that reflect listening intent. Increasing evidence also shows that the negative impact of hearing deficits on mobility, gait, and balance may be mitigated by prosthetic hearing device intervention. Better understanding of the relationships between head movement, full body kinetics, and hearing health, should lead to improved signal processing strategies across a range of assistive and augmented hearing devices. The purpose of this review is to introduce the wider hearing community to the kinesiology of head movement and to place it in the context of hearing and communication with the goal of expanding the field of ecologically-specific listener behavior.