Age-related hearing loss and "hearables": An agenda for moral considerations.

Rapid advances in digital hearing technologies, also known as hearables, are expected to disrupt the direct-to-consumer health market. For older adults with higher incidence of hearing loss, such disruption could reduce hearing problems, increase accessibility to hearing aids, and mitigate related stigmas. This paper delves into the intersection of disruptive innovation and hearables within the realm of biomedical ethics. Through a comprehensive exploration, we shed light on the ethical implications surrounding hearables. By critically evaluating the key ethical advantages and drawbacks, we find that no single concern presents an insurmountable a priori objection to hearables. We conclude with some ideas to maximize the benefits of hearables and further promote opportunities for equitable hearing health.

Consumer and audiologist perspectives on hearables: a qualitative study.

OBJECTIVE: We aimed to explore (i) what adults with hearing difficulties want and need from hearables, which we defined as any non-medical personal sound amplification product, and (ii) what hearing care professionals think about hearables. DESIGN: This was an exploratory, qualitative study conducted using separate focus groups with adults with hearing difficulties and audiologists. Data were analysed inductively using reflexive thematic analysis. STUDY SAMPLE: Participants were 12 adults with hearing difficulties and 6 audiologists. RESULTS: Adults with hearing difficulties expressed desire for trustworthy information and support, described evaluating hearables and other devices according to diverse personal criteria, and expressed willingness to vary their budget according to product quality. Audiologists expressed views that hearables are an inferior product but useful tool, that it is not necessarily their role to assist with hearables, that hearables are a source of uncertainty, and that the provision of hearables by audiologists is not currently practical. CONCLUSION: Adults with hearing difficulties may have complex reasons for considering hearables and may desire a high level of clinical support in this area. Ongoing research into the efficacy and effectiveness of hearables is needed together with research into effective strategies to incorporate hearables into clinical practice.

Brain Wearables: Validation Toolkit for Ear-Level EEG Sensors.

EEG-enabled earbuds represent a promising frontier in brain activity monitoring beyond traditional laboratory testing. Their discrete form factor and proximity to the brain make them the ideal candidate for the first generation of discrete non-invasive brain-computer interfaces (BCIs). However, this new technology will require comprehensive characterization before we see widespread consumer and health-related usage. To address this need, we developed a validation toolkit that aims to facilitate and expand the assessment of ear-EEG devices. The first component of this toolkit is a desktop application ("EaR-P Lab") that controls several EEG validation paradigms. This application uses the Lab Streaming Layer (LSL) protocol, making it compatible with most current EEG systems. The second element of the toolkit introduces an adaptation of the phantom evaluation concept to the domain of ear-EEGs. Specifically, it utilizes 3D scans of the test subjects' ears to simulate typical EEG activity around and inside the ear, allowing for controlled assessment of different ear-EEG form factors and sensor configurations. Each of the EEG paradigms were validated using wet-electrode ear-EEG recordings and benchmarked against scalp-EEG measurements. The ear-EEG phantom was successful in acquiring performance metrics for hardware characterization, revealing differences in performance based on electrode location. This information was leveraged to optimize the electrode reference configuration, resulting in increased auditory steady-state response (ASSR) power. Through this work, an ear-EEG evaluation toolkit is made available with the intention to facilitate the systematic assessment of novel ear-EEG devices from hardware to neural signal acquisition.

HearCough: Enabling continuous cough event detection on edge computing hearables.

Cough event detection is the foundation of any measurement associated with cough, one of the primary symptoms of pulmonary illnesses. This paper proposes HearCough, which enables continuous cough event detection on edge computing hearables, by leveraging always-on active noise cancellation (ANC) microphones in commodity hearables. Specifically, we proposed a lightweight end-to-end neural network model - Tiny-COUNET and its transfer learning based traning method. When evaluated on our acted cough event dataset, Tiny-COUNET achieved equivalent detection performance but required significantly less computational resources and storage space than cutting-edge cough event detection methods. Then we implemented HearCough by quantifying and deploying the pre-trained Tiny-COUNET to a popular micro-controller in consumer hearables. Lastly, we evaluated that HearCough is effective and reliable for continuous cough event detection through a field study with 8 patients. HearCough achieved 2 Hz cough event detection with an accuracy of 90.0% and an F1-score of 89.5% by consuming an additional 5.2 mW power. We envision HearCough as a low-cost add-on for future hearables to enable continuous cough detection and pulmonary health monitoring.

The value of headphone accommodations in Apple Airpods Pro for managing speech-in-noise hearing difficulties of individuals with normal audiograms.

OBJECTIVE: To investigate the extent to which Headphone Accommodations in Apple AirPods Pro attend to the hearing needs of individuals with normal audiograms who experience hearing difficulties in noisy environments. DESIGN: Single-arm interventional study using acoustic measures, speech-in-noise laboratory testing, and real-world measures via questionnaires and ecological momentary assessment. STUDY SAMPLE: Seventeen normal-hearing individuals (9 female, 21-59 years) with self-reported hearing-in-noise difficulties. RESULTS: Acoustic measures showed that, relative to unaided, AirPods Pro provided a SNR advantage of +5.4 dB. Speech intelligibility performance in laboratory testing increased 11.8% with AirPods Pro, relative to unaided. On average, participants trialling AirPods Pro in real-world noisy venues reported that their overall hearing experience was a bit better than without them. Five participants (29%) reported that they would continue using AirPods Pro in the future. The most relevant barriers that would discourage their future use were limited hearing benefit, discomfort, and stigma. CONCLUSIONS: Occasional use of AirPods Pro may help some individuals with normal audiograms ameliorate their speech-in-noise hearing difficulties. The identified barriers may inspire the development of new technological solutions aimed at providing an optimal management strategy for the hearing difficulties of this segment of the population.

An In-Ear PPG-Based Blood Glucose Monitor: A Proof-of-Concept Study.

Monitoring diabetes saves lives. To this end, we introduce a novel, unobtrusive, and readily deployable in-ear device for the continuous and non-invasive measurement of blood glucose levels (BGLs). The device is equipped with a low-cost commercially available pulse oximeter whose infrared wavelength (880 nm) is used for the acquisition of photoplethysmography (PPG). For rigor, we considered a full range of diabetic conditions (non-diabetic, pre-diabetic, type I diabetic, and type II diabetic). Recordings spanned nine different days, starting in the morning while fasting, up to a minimum of a two-hour period after eating a carbohydrate-rich breakfast. The BGLs from PPG were estimated using a suite of regression-based machine learning models, which were trained on characteristic features of PPG cycles pertaining to high and low BGLs. The analysis shows that, as desired, an average of 82% of the BGLs estimated from PPG lie in region A of the Clarke error grid (CEG) plot, with 100% of the estimated BGLs in the clinically acceptable CEG regions A and B. These results demonstrate the potential of the ear canal as a site for non-invasive blood glucose monitoring.

In-Ear SpO2: A Tool for Wearable, Unobtrusive Monitoring of Core Blood Oxygen Saturation.

The non-invasive estimation of blood oxygen saturation (SpO2) by pulse oximetry is of vital importance clinically, from the detection of sleep apnea to the recent ambulatory monitoring of hypoxemia in the delayed post-infective phase of COVID-19. In this proof of concept study, we set out to establish the feasibility of SpO2 measurement from the ear canal as a convenient site for long term monitoring, and perform a comprehensive comparison with the right index finger-the conventional clinical measurement site. During resting blood oxygen saturation estimation, we found a root mean square difference of 1.47% between the two measurement sites, with a mean difference of 0.23% higher SpO2 in the right ear canal. Using breath holds, we observe the known phenomena of time delay between central circulation and peripheral circulation with a mean delay between the ear and finger of 12.4 s across all subjects. Furthermore, we document the lower photoplethysmogram amplitude from the ear canal and suggest ways to mitigate this issue. In conjunction with the well-known robustness to temperature induced vasoconstriction, this makes conclusive evidence for in-ear SpO2 monitoring being both convenient and superior to conventional finger measurement for continuous non-intrusive monitoring in both clinical and everyday-life settings.

Hearables: feasibility of recording cardiac rhythms from single in-ear locations.

The ear is well positioned to accommodate both brain and vital signs monitoring, via so-called hearable devices. Consequently, ear-based electroencephalography has recently garnered great interest. However, despite the considerable potential of hearable based cardiac monitoring, the biophysics and characteristic cardiac rhythm of ear-based electrocardiography (ECG) are not yet well understood. To this end, we map the cardiac potential on the ear through volume conductor modelling and measurements on multiple subjects. In addition, in order to demonstrate real-world feasibility of in-ear ECG, measurements are conducted throughout a long-time simulated driving task. As a means of evaluation, the correspondence between the cardiac rhythms obtained via the ear-based and standard Lead I measurements, with respect to the shape and timing of the cardiac rhythm, is verified through three measures of similarity: the Pearson correlation, and measures of amplitude and timing deviations. A high correspondence between the cardiac rhythms obtained via the ear-based and Lead I measurements is rigorously confirmed through agreement between simulation and measurement, while the real-world feasibility was conclusively demonstrated through efficacious cardiac rhythm monitoring during prolonged driving. This work opens new avenues for seamless, hearable-based cardiac monitoring that extends beyond heart rate detection to offer cardiac rhythm examination in the community.

From Scalp to Ear-EEG: A Generalizable Transfer Learning Model for Automatic Sleep Scoring in Older People.

OBJECTIVE: Sleep monitoring has extensively utilized electroencephalogram (EEG) data collected from the scalp, yielding very large data repositories and well-trained analysis models. Yet, this wealth of data is lacking for emerging, less intrusive modalities, such as ear-EEG. METHODS AND PROCEDURES: The current study seeks to harness the abundance of open-source scalp EEG datasets by applying models pre-trained on data, either directly or with minimal fine-tuning; this is achieved in the context of effective sleep analysis from ear-EEG data that was recorded using a single in-ear electrode, referenced to the ipsilateral mastoid, and developed in-house as described in our previous work. Unlike previous studies, our research uniquely focuses on an older cohort (17 subjects aged 65-83, mean age 71.8 years, some with health conditions), and employs LightGBM for transfer learning, diverging from previous deep learning approaches. RESULTS: Results show that the initial accuracy of the pre-trained model on ear-EEG was 70.1%, but fine-tuning the model with ear-EEG data improved its classification accuracy to 73.7%. The fine-tuned model exhibited a statistically significant improvement (p < 0.05, dependent t-test) for 10 out of the 13 participants, as reflected by an enhanced average Cohen's kappa score (a statistical measure of inter-rater agreement for categorical items) of 0.639, indicating a stronger agreement between automated and expert classifications of sleep stages. Comparative SHAP value analysis revealed a shift in feature importance for the N3 sleep stage, underscoring the effectiveness of the fine-tuning process. CONCLUSION: Our findings underscore the potential of fine-tuning pre-trained scalp EEG models on ear-EEG data to enhance classification accuracy, particularly within an older population and using feature-based methods for transfer learning. This approach presents a promising avenue for ear-EEG analysis in sleep studies, offering new insights into the applicability of transfer learning across different populations and computational techniques. CLINICAL IMPACT: An enhanced ear-EEG method could be pivotal in remote monitoring settings, allowing for continuous, non-invasive sleep quality assessment in elderly patients with conditions like dementia or sleep apnea.

Apple AirPods Pro as a Hearing Assistive Device in Patients with Mild to Moderate Hearing Loss.

PURPOSE: This study aimed to assess the feasibility of the Apple AirPods Pro with the headphone accommodation feature as a hearing assistive device for patients with mild to moderate hearing loss (HL). MATERIALS AND METHODS: The study included a total of 35 participants with mild to moderate HL. To determine the degree of HL in the participants, a screening test using pure-tone audiometry was conducted prior to the main tests of functional gain, word recognition score (WRS), and sentence recognition in noisy environments. The study employed two hearing devices: the Bean (a personal sound amplification product, PSAP) and the AirPods Pro. RESULTS: Regarding functional gain, there were no significant differences between the Bean and the AirPods Pro at all frequencies, except 8 kHz. In terms of WRS, both the Bean and the AirPods Pro had higher scores than the unaided condition. In sentence recognition, both the Bean and the AirPods Pro had higher scores than the unaided condition. During real-ear measurement, the Bean demonstrated consistent frequency responses, while the AirPods had a deviation exceeding 10 dB SPL at 6 kHz in the left ear. This deviation was absent for all other frequencies. CONCLUSION: This study shows that the Apple AirPods Pro, with its headphone accommodation feature, performed similarly to a validated PSAP and improved hearing compared to unaided conditions.

Hearables as a Gateway to Hearing Health Care.

The market for hearing technology is evolving-with the emergence of hearables, it now extends beyond hearing aids and includes any ear-level devices with wireless connectivity (i.e., wireless earbuds). However, will this evolving marketplace bring forth opportunities or challenges to individuals' hearing health care and the profession of audiology and otolaryngology? The debate has been ongoing. This study explores the wide spectrum of hearables available in the market and discusses the necessity of high-quality clinical evidence prior to the implementation of over-the-counter devices into clinical practice.

The hunt for hidden hearing loss in humans: From preclinical studies to effective interventions.

Many individuals experience hearing problems that are hidden under a normal audiogram. This not only impacts on individual sufferers, but also on clinicians who can offer little in the way of support. Animal studies using invasive methodologies have developed solid evidence for a range of pathologies underlying this hidden hearing loss (HHL), including cochlear synaptopathy, auditory nerve demyelination, elevated central gain, and neural mal-adaptation. Despite progress in pre-clinical models, evidence supporting the existence of HHL in humans remains inconclusive, and clinicians lack any non-invasive biomarkers sensitive to HHL, as well as a standardized protocol to manage hearing problems in the absence of elevated hearing thresholds. Here, we review animal models of HHL as well as the ongoing research for tools with which to diagnose and manage hearing difficulties associated with HHL. We also discuss new research opportunities facilitated by recent methodological tools that may overcome a series of barriers that have hampered meaningful progress in diagnosing and treating of HHL.

Instrumental Quality Predictions and Analysis of Auditory Cues for Algorithms in Modern Headphone Technology.

Smart headphones or hearables use different types of algorithms such as noise cancelation, feedback suppression, and sound pressure equalization to eliminate undesired sound sources or to achieve acoustical transparency. Such signal processing strategies might alter the spectral composition or interaural differences of the original sound, which might be perceived by listeners as monaural or binaural distortions and thus degrade audio quality. To evaluate the perceptual impact of these distortions, subjective quality ratings can be used, but these are time consuming and costly. Auditory-inspired instrumental quality measures can be applied with less effort and may also be helpful in identifying whether the distortions impair the auditory representation of monaural or binaural cues. Therefore, the goals of this study were (a) to assess the applicability of various monaural and binaural audio quality models to distortions typically occurring in hearables and (b) to examine the effect of those distortions on the auditory representation of spectral, temporal, and binaural cues. Results showed that the signal processing algorithms considered in this study mainly impaired (monaural) spectral cues. Consequently, monaural audio quality models that capture spectral distortions achieved the best prediction performance. A recent audio quality model that predicts monaural and binaural aspects of quality was revised based on parts of the current data involving binaural audio quality aspects, leading to improved overall performance indicated by a mean Pearson linear correlation of 0.89 between obtained and predicted ratings.

Hearables, in-ear sensing devices for bio-signal acquisition: a narrative review.

INTRODUCTION: Hearables are ear devices used for multiple purposes including ubiquitous/remote monitoring of vital signals. This can support early detection, prevention, and management of urgent/non-urgent healthcare needs. This review therefore seeks to analyze the challenges and capabilities of hearables used to monitor human physiological signals. AREAS COVERED: Studies were identified via search (Medline, Embase, Web of Science, Cochrane Library, Scopus) and conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Bias assessment used the Mixed Methods Appraisal Tool 2018 and Quality Assessment of Diagnostic Accuracy Studies 2nd Edition. 92/631 studies met the inclusion criteria and were qualitatively analyzed. The outcomes, applications, advantages, and limitations were discussed according to the vital signal measured. The bias risk ranged from low to high, with most studies facing moderate-to-high risk in subject selection due to small sample sizes. EXPERT OPINION: Most studies reported good outcomes for ear signal acquisition compared to reference devices. To improve practicability and implementation, wireless connectivity, battery life, impact of motion/environmental artifacts and comfort need to be addressed going forward. Hearable technologies have also shown potential synergies with hearing aids. In future, multimodal ear-sensing devices opens the possibility of comprehensive health monitoring within daily life.

Insight into the use of tympanic temperature during target temperature management in emergency and critical care: a scoping review.

BACKGROUND: Target temperature management (TTM) is suggested to reduce brain damage in the presence of global or local ischemia. Prompt TTM application may help to improve outcomes, but it is often hindered by technical problems, mainly related to the portability of cooling devices and temperature monitoring systems. Tympanic temperature (TTy) measurement may represent a practical, non-invasive approach for core temperature monitoring in emergency settings, but its accuracy under different TTM protocols is poorly characterized. The present scoping review aimed to collect the available evidence about TTy monitoring in TTM to describe the technique diffusion in various TTM contexts and its accuracy in comparison with other body sites under different cooling protocols and clinical conditions. METHODS: The scoping review was conducted following the guidelines of the Preferred Reporting Items for Systematic Review and Meta-Analysis extension for scoping reviews (PRISMA-ScR). PubMed, Scopus, and Web of Science electronic databases were systematically searched to identify studies conducted in the last 20 years, where TTy was measured in TTM context with specific focus on pre-hospital or in-hospital emergency settings. RESULTS: The systematic search identified 35 studies, 12 performing TTy measurements during TTM in healthy subjects, 17 in patients with acute cardiovascular events, and 6 in patients with acute neurological diseases. The studies showed that TTy was able to track temperature changes induced by either local or whole-body cooling approaches in both pre-hospital and in-hospital settings. Direct comparisons to other core temperature measurements from other body sites were available in 22 studies, which showed a faster and larger change of TTy upon TTM compared to other core temperature measurements. Direct brain temperature measurements were available only in 3 studies and showed a good correlation between TTy and brain temperature, although TTy displayed a tendency to overestimate cooling effects compared to brain temperature. CONCLUSIONS: TTy was capable to track temperature changes under a variety of TTM protocols and clinical conditions in both pre-hospital and in-hospital settings. Due to the heterogeneity and paucity of comparative temperature data, future studies are needed to fully elucidate the advantages of TTy in emergency settings and its capability to track brain temperature.

Emerging Technologies, Market Segments, and MarkeTrak 10 Insights in Hearing Health Technology.

Hearing health care is rapidly changing through innovation in technology, services, business models, and product categories. The introduction of hearables and over-the-counter (OTC) hearing aids in particular will change the market for hearing help and the role of the hearing care professionals (HCPs). This article focuses on how these products will be differentiated from HCP-fit hearing aids through their ability to address the unmet needs of different consumer segments within the population of people with hearing dysfunction. The unmet hearing needs of each segment are discussed, and the size of each segment estimated, demonstrating a large potential market for hearables and a smaller potential market for hearing aids than has been previously mentioned in the literature. The results from MarkeTrak 10's survey of consumers' attitudes toward an OTC model are reviewed, showing that approximately half of both hearing aid owners and nonowners are uncomfortable doing hearing- and hearing aid-related tasks on their own without the assistance of an HCP and would be unlikely to purchase OTC hearing aids if available today. MarkeTrak data are also shown that demonstrate that the majority of hearing aid and personal sound amplification product owners believe that the HCP helped or would have helped with their hearing devices. Finally, challenges to OTC hearing aids becoming successful are discussed.

On Mitigating Acoustic Feedback in Hearing Aids with Frequency Warping by All-Pass Networks.

Acoustic feedback control continues to be a challenging problem due to the emerging form factors in advanced hearing aids (HAs) and hearables. In this paper, we present a novel use of well-known all-pass filters in a network to perform frequency warping that we call "freping." Freping helps in breaking the Nyquist stability criterion and improves adaptive feedback cancellation (AFC). Based on informal subjective assessments, distortions due to freping are fairly benign. While common objective metrics like the perceptual evaluation of speech quality (PESQ) and the hearing-aid speech quality index (HASQI) may not adequately capture distortions due to freping and acoustic feedback artifacts from a perceptual perspective, they are still instructive in assessing the proposed method. We demonstrate quality improvements with freping for a basic AFC (PESQ: 2.56 to 3.52 and HASQI: 0.65 to 0.78) at a gain setting of 20; and an advanced AFC (PESQ: 2.75 to 3.17 and HASQI: 0.66 to 0.73) for a gain of 30. From our investigations, freping provides larger improvement for basic AFC, but still improves overall system performance for many AFC approaches.

A Wearable Platform for Research in Augmented Hearing.

We have previously reported a realtime, open-source speech-processing platform (OSP) for hearing aids (HAs) research. In this contribution, we describe a wearable version of this platform to facilitate audiological studies in the lab and in the field. The system is based on smartphone chipsets to leverage power efficiency in terms of FLOPS/watt and economies of scale. We present the system architecture and discuss salient design elements in support of HA research. The ear-level assemblies support up to 4 microphones on each ear, with 96 kHz, 24 bit codecs. The wearable unit runs OSP Release 2018c on top of 64-bit Debian Linux for binaural HA with an overall latency of 5.6 ms. The wearable unit also hosts an embedded web server (EWS) to monitor and control the HA state in realtime. We describe three example web apps in support of typical audiological studies they enable. Finally, we describe a baseline speech enhancement module included with Release 2018c, and describe extensions to the algorithms as future work.