SCH: INT: Collaborative Research: A Framework for Optimizing Hearing Aids In Situ Based on Patient Feedback, Auditory Context, and Audiologist Input

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Twenty percent of Americans will be 65 years or older by 2030 out of which 35 - 50% will report having age-related hearing impairment that is treated primarily with hearing aids (HA). Regular use of HAs has been shown to improve communication and avoid the negative effects of hearing loss that include an increased risk of social isolation, depression, and inability to work, travel, or be physically active. Past research has shown that many people do not wear their HAs regularly, as they are unsatisfied with the performance in the real world. A fundamental limitation of existing methods for tuning HA is that they are not tailored to individual needs, which often leads to unsatisfactory performance. As part of this project, a team of computer scientists, engineers, and audiologists will develop new methods for tuning HAs that are based on the individual needs of a patient.The project is based on the approach that better HA configurations may be identified based on feedback from the patient that is collected in the moment and in situ. The project includes development of two systems for optimizing the configuration of HAs. The first system will improve the efficacy of the traditional tuning process by improving how feedback is obtained from patients, combined with auditory context information, and translated into HA configuration adjustments. The second system will automatically adapt the configuration of a HA based on patient feedback and auditory context information without requiring an audiologist to perform adjustments. The intellectual merit of this proposal includes the advances in machine learning necessary to model the performance of complex systems that have numerous parameters such as HAs and tuning their parameters based on feedback obtained from patients. Additionally, the project will advance the state-of-the-art in embedded systems by developing techniques to run HA optimization algorithms as part of a multi-tier system composed of HAs, mobile phones, and cloud services. It is anticipated that the proposed research will empower patients to become more involved in their hearing care, improve HA satisfaction, and enrich their social interactions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.