Personalizing the Fitting of Hearing Aids by Learning Contextual Preferences From Internet of Things Data

Research output: Contribution to journalJournal article – Annual report year: 2018Researchpeer-review

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Personalizing the Fitting of Hearing Aids by Learning Contextual Preferences From Internet of Things Data. / Johansen, Benjamin; Petersen, Michael Kai; Korzepa, Maciej Jan; Larsen, Jan; Henrik Pontoppidan, Niels; Larsen, Jakob Eg.

In: Computers, Vol. 7, No. 1, 2017.

Research output: Contribution to journalJournal article – Annual report year: 2018Researchpeer-review

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@article{b3c39aae6cbb4a7e9f3540a93c84630f,
title = "Personalizing the Fitting of Hearing Aids by Learning Contextual Preferences From Internet of Things Data",
abstract = "The lack of individualized fitting of hearing aids results in many patients never getting the intended benefits, in turn causing the devices to be left unused in a drawer. However, living with an untreated hearing loss has been found to be one of the leading lifestyle related causes of dementia and cognitive decline. Taking a radically different approach to personalize the fitting process of hearing aids, by learning contextual preferences from user-generated data, we in this paper outline the results obtained through a 9-month pilot study. Empowering the user to select between several settings using Internet of things (IoT) connected hearing aids allows for modeling individual preferences and thereby identifying distinct coping strategies. These behavioral patterns indicate that users prefer to switch between highly contrasting aspects of omnidirectionality and noise reduction dependent on the context, rather than relying on the medium “one size fits all” program frequently provided by default in hearing health care. We argue that an IoT approach facilitated by the usage of smartphones may constitute a paradigm shift, enabling continuous personalization of settings dependent on the changing context. Furthermore, making the user an active part of the fitting solution based on self-tracking may increase engagement and awareness and thus improve the quality of life for hearing impaired users.",
keywords = "Quantified self, Hearables, Sound augmentation, Behavior patterns",
author = "Benjamin Johansen and Petersen, {Michael Kai} and Korzepa, {Maciej Jan} and Jan Larsen and {Henrik Pontoppidan}, Niels and Larsen, {Jakob Eg}",
year = "2017",
doi = "10.3390/computers7010001",
language = "English",
volume = "7",
journal = "Computers",
issn = "2073-431X",
publisher = "M D P I AG",
number = "1",

}

RIS

TY - JOUR

T1 - Personalizing the Fitting of Hearing Aids by Learning Contextual Preferences From Internet of Things Data

AU - Johansen, Benjamin

AU - Petersen, Michael Kai

AU - Korzepa, Maciej Jan

AU - Larsen, Jan

AU - Henrik Pontoppidan, Niels

AU - Larsen, Jakob Eg

PY - 2017

Y1 - 2017

N2 - The lack of individualized fitting of hearing aids results in many patients never getting the intended benefits, in turn causing the devices to be left unused in a drawer. However, living with an untreated hearing loss has been found to be one of the leading lifestyle related causes of dementia and cognitive decline. Taking a radically different approach to personalize the fitting process of hearing aids, by learning contextual preferences from user-generated data, we in this paper outline the results obtained through a 9-month pilot study. Empowering the user to select between several settings using Internet of things (IoT) connected hearing aids allows for modeling individual preferences and thereby identifying distinct coping strategies. These behavioral patterns indicate that users prefer to switch between highly contrasting aspects of omnidirectionality and noise reduction dependent on the context, rather than relying on the medium “one size fits all” program frequently provided by default in hearing health care. We argue that an IoT approach facilitated by the usage of smartphones may constitute a paradigm shift, enabling continuous personalization of settings dependent on the changing context. Furthermore, making the user an active part of the fitting solution based on self-tracking may increase engagement and awareness and thus improve the quality of life for hearing impaired users.

AB - The lack of individualized fitting of hearing aids results in many patients never getting the intended benefits, in turn causing the devices to be left unused in a drawer. However, living with an untreated hearing loss has been found to be one of the leading lifestyle related causes of dementia and cognitive decline. Taking a radically different approach to personalize the fitting process of hearing aids, by learning contextual preferences from user-generated data, we in this paper outline the results obtained through a 9-month pilot study. Empowering the user to select between several settings using Internet of things (IoT) connected hearing aids allows for modeling individual preferences and thereby identifying distinct coping strategies. These behavioral patterns indicate that users prefer to switch between highly contrasting aspects of omnidirectionality and noise reduction dependent on the context, rather than relying on the medium “one size fits all” program frequently provided by default in hearing health care. We argue that an IoT approach facilitated by the usage of smartphones may constitute a paradigm shift, enabling continuous personalization of settings dependent on the changing context. Furthermore, making the user an active part of the fitting solution based on self-tracking may increase engagement and awareness and thus improve the quality of life for hearing impaired users.

KW - Quantified self

KW - Hearables

KW - Sound augmentation

KW - Behavior patterns

U2 - 10.3390/computers7010001

DO - 10.3390/computers7010001

M3 - Journal article

VL - 7

JO - Computers

JF - Computers

SN - 2073-431X

IS - 1

ER -