Rethinking Hearing Aid Fitting by Learning From Behavioral Patterns

Benjamin Johansen, Michael Kai Petersen, Niels Henrik Pontoppidan, Per Sandholm, Jakob Eg Larsen

Research output: Chapter in Book/Report/Conference proceedingConference abstract in proceedingsResearchpeer-review

Abstract

The recent introduction of Internet connected hearing instruments offers a paradigm shift in hearing instrument fitting. Potentially this makes it possible for devices to adapt their settings to a changing context, inferred from user interactions. In a pilot study we enabled hearing instrument users to remotely enhance auditory focus and attenuate background noise to improve speech intelligibility. N=5, participants changed program settings and adjusted volume on their hearing instruments using their smartphones. We found that individual behavioral patterns affected the usage of the devices. A significant difference between program usage, and weekdays versus weekends, were found. Users not only changed programs to modify aspects of directionality and noise reduction, but also continuously adjusted the volume. Rethinking hearing instruments as devices that adaptively learn behavioral patterns based on user interaction, might provide a degree of personalization that has not been feasible due to lack of audiological resources.
Original languageEnglish
Title of host publicationCHI’17 Extended Abstracts
Number of pages7
PublisherAssociation for Computing Machinery
Publication date2017
Pages1733-1739
ISBN (Print)978-1-4503-4656-6
DOIs
Publication statusPublished - 2017
EventACM CHI 2017 - Colorado Convention Center, Denver, United States
Duration: 6 May 201711 May 2017

Conference

ConferenceACM CHI 2017
LocationColorado Convention Center
CountryUnited States
CityDenver
Period06/05/201711/05/2017

Keywords

  • Hearing impairment
  • User behavior
  • Health
  • Aging
  • Augmented audio

Cite this

Johansen, B., Petersen, M. K., Pontoppidan, N. H., Sandholm, P., & Larsen, J. E. (2017). Rethinking Hearing Aid Fitting by Learning From Behavioral Patterns. In CHI’17 Extended Abstracts (pp. 1733-1739). Association for Computing Machinery. https://doi.org/10.1145/3027063.3053156