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Adults with mild-to-moderate hearing loss can use over-the-counter hearing aids to treat their hearing loss at a fraction of traditional hearing care costs. These products incorporate self-fitting methods that allow end-users to configure their hearing aids without the help of an audiologist. A self-fitting method helps users configure the gain-frequency responses that control the amplification for each frequency band of the incoming sound. This paper considers how to guide the design of self-fitting methods by evaluating certain aspects of their design using computational tools before performing user studies. Most existing fitting methods provide various user interfaces to allow users to select a configuration from a predetermined set of presets. Accordingly, it is essential for the presets to meet the hearing needs of a large fraction of users who suffer from varying degrees of hearing loss and have unique hearing preferences. To this end, we propose a novel metric for evaluating the effectiveness of preset-based approaches by computing their population coverage. The population coverage estimates the fraction of users for which a self-fitting method can find a configuration they prefer. A unique aspect of our approach is a probabilistic model that captures how a user's unique preferences differ from other users with similar hearing loss. Next, we propose methods for building preset-based and slider-based self-fitting methods that maximize the population coverage. Simulation results demonstrate that the proposed algorithms can effectively select a small number of presets that provide higher population coverage than clustering-based approaches. Moreover, we may use our algorithms to configure the number of increments of slider-based methods. We expect that the computational tools presented in this article will help reduce the cost of developing new self-fitting methods by allowing researchers to evaluate population coverage before performing user studies.
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Comparing population coverage between hearing aids using presets vs Bose CustomTune.
The self-fitting Bose SoundControl™ hearing aid is the first of its kind to gain FDA clearance. In the self-fitting process, the Bose Hear app uses the Bose CustomTune™ interface for mapping to a wide range of target gain profiles, derived from a hearing loss database. This article compares the population coverage—or the percentage of people who would be able to find a frequency gain profile similar to a NAL-NL2 prescription fit—of SoundControl to other self-fitting amplification devices which typically feature only 1 to 4 preset gain profiles.
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- Award ID(s):
- 1838830
- PAR ID:
- 10309718
- Date Published:
- Journal Name:
- Hearing review
- Volume:
- 28
- Issue:
- 6
- ISSN:
- 1178-6698
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Introduction Using data collected from hearing aid users’ own hearing aids could improve the customization of hearing aid processing for different users based on the auditory environments they encounter in daily life. Prior studies characterizing hearing aid users’ auditory environments have focused on mean sound pressure levels and proportions of environments based on classifications. In this study, we extend these approaches by introducing entropy to quantify the diversity of auditory environments hearing aid users encounter. Materials and Methods Participants from 4 groups (younger listeners with normal hearing and older listeners with hearing loss from an urban or rural area) wore research hearing aids and completed ecological momentary assessments on a smartphone for 1 week. The smartphone was programmed to sample the processing state (input sound pressure level and environment classification) of the hearing aids every 10 min and deliver an ecological momentary assessment every 40 min. Entropy values for sound pressure levels, environment classifications, and ecological momentary assessment responses were calculated for each participant to quantify the diversity of auditory environments encountered over the course of the week. Entropy values between groups were compared. Group differences in entropy were compared to prior work reporting differences in mean sound pressure levels and proportions of environment classifications. Group differences in entropy measured objectively from the hearing aid data were also compared to differences in entropy measured from the self-report ecological momentary assessment data. Results Auditory environment diversity, quantified using entropy from the hearing aid data, was significantly higher for younger listeners than older listeners. Entropy measured using ecological momentary assessment was also significantly higher for younger listeners than older listeners. Discussion Using entropy, we show that younger listeners experience a greater diversity of auditory environments than older listeners. Alignment of group entropy differences with differences in sound pressure levels and hearing aid feature activation previously reported, along with alignment with ecological momentary response entropy, suggests that entropy is a valid and useful metric. We conclude that entropy is a simple and intuitive way to measure auditory environment diversity using hearing aid data.more » « less
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