Personalizing over-the-counter hearing aids using pairwise comparisons

Vyas, Dhruv; Brummet, Ryan; Anwar, Yumna; Jensen, J. Hartvig; Jorgensen, Erik; Wu, Yu-Hsiang; Chipara, Octav

doi:10.1016/j.smhl.2021.100231

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…In [ 40 ], over-the-counter hearing aids, intended to make hearing healthcare more affordable and accessible, were evaluated by allowing end-users to configure such hearing aids themselves. An approach involving the division of a 24-dimensional configuration space into presets was considered to meet the needs of a significant portion of individuals with mild-to-moderate hearing loss.…”

Section: Methods: Personalization Of Amplification In Hearing Aidsmentioning

confidence: 99%

A Review of Machine Learning Approaches for the Personalization of Amplification in Hearing Aids

Tasnim,

Ni,

Lobarinas

et al. 2024

Sensors

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This paper provides a review of various machine learning approaches that have appeared in the literature aimed at individualizing or personalizing the amplification settings of hearing aids. After stating the limitations associated with the current one-size-fits-all settings of hearing aid prescriptions, a spectrum of studies in engineering and hearing science are discussed. These studies involve making adjustments to prescriptive values in order to enable preferred and individualized settings for a hearing aid user in an audio environment of interest to that user. This review gathers, in one place, a comprehensive collection of works that have been conducted thus far with respect to achieving the personalization or individualization of the amplification function of hearing aids. Furthermore, it underscores the impact that machine learning can have on enabling an improved and personalized hearing experience for hearing aid users. This paper concludes by stating the challenges and future research directions in this area.

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Section: Methods: Personalization Of Amplification In Hearing Aidsmentioning

confidence: 99%

A Review of Machine Learning Approaches for the Personalization of Amplification in Hearing Aids

Tasnim,

Ni,

Lobarinas

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Among these methods, the pairwise comparison method is often chosen due to its simplicity in hearing preference studies, e.g. [9][10][11][12]. This method places minimal cognitive load on users as it merely involves selecting one out of two options similar to the pairwise comparisons in an eye exam.…”

Section: Introductionmentioning

confidence: 99%

Personalization of Hearing Aid Fitting Based on Adaptive Dynamic Range Optimization

Akbarzadeh

Lobariñas

et al. 2022

Sensors

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Adaptive dynamic range optimization (ADRO) is a hearing aid fitting rationale which involves adjusting the gains in a number of frequency bands by using a series of rules. The rules reflect the comparison of the estimated percentile occurrences of the sound levels with the audibility and comfort hearing levels of a person suffering from hearing loss. In the study reported in this paper, a previously developed machine learning method was utilized to personalize the ADRO fitting in order to provide an improved hearing experience as compared to the standard ADRO hearing aid fitting. The personalization was carried out based on the user preference model within the framework of maximum likelihood inverse reinforcement learning. The testing of ten subjects with hearing loss was conducted, which indicated that the personalized ADRO was preferred over the standard ADRO on average by about 10 times. Furthermore, a word recognition experiment was conducted, which showed that the personalized ADRO had no adverse impact on speech understanding as compared to the standard ADRO.

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Over‐The‐Counter Hearing Aids: Are They Safe and Effective?

Shah,

Wilkins,

Panth

et al. 2024

Otolaryngol.--head neck surg.

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ObjectiveIn 2022, the Food and Drug Administration established a new regulatory category for over‐the‐counter (OTC) hearing aids for mild to moderate hearing loss. Herein, we aim to better compare the safety and efficacy of these devices to that of prescription hearing aids.Study DesignComparative‐effectiveness model.SettingAcademic Audiology Center.MethodsThe safety and efficacy of prescription and OTC hearing aids was compared using the AudioScan Verifit 2 Testbox software. Three types of hearing loss (downsloping, sharp downsloping, and reverse sloping) were analyzed. Efficacy was tested at 3 volume inputs and was measured by calculating the average difference in test points (produced by the devices) and target points (estimated by the software). Safety was assessed by calculating the average difference in test points and the maximally safe hearing level (produced by the software).ResultsPrescription hearing aids were found to have a better safety profile by being further from the safety threshold compared to OTC devices at the 8000 Hz frequency for the 2 types of downsloping hearing loss patterns studied (48 vs 30.5 dB, P = .04; 51 vs 32.5 dB, P = .03). Prescription hearing aids also carried a statistically significant advantage at 3 test points. OTC hearing aids generally had a greater difference between test and target points.ConclusionOTC and prescription hearing aids are comparably safe, though OTC hearing aids are slightly less efficacious. Further evaluation of the OTC hearing aid efficacy is warranted to ensure it provides the gain of benefit needed for different types of hearing loss.

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Personalizing over-the-counter hearing aids using pairwise comparisons

Cited by 5 publications

References 20 publications

A Review of Machine Learning Approaches for the Personalization of Amplification in Hearing Aids

A Review of Machine Learning Approaches for the Personalization of Amplification in Hearing Aids

Personalization of Hearing Aid Fitting Based on Adaptive Dynamic Range Optimization

Over‐The‐Counter Hearing Aids: Are They Safe and Effective?

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