國立虎尾科技大學 |

Mobile Ecological Momentary Assessment for Hearing Aid Evaluation.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Mobile Ecological Momentary Assessment for Hearing Aid Evaluation./
Author:	Hasan, Syed Shabih.
Description:	1 online resource (153 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.
Contained By:	Dissertation Abstracts International78-12B(E).
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9780355107555

Mobile Ecological Momentary Assessment for Hearing Aid Evaluation.
Hasan, Syed Shabih.

Mobile Ecological Momentary Assessment for Hearing Aid Evaluation. - 1 online resource (153 pages)

Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Hearing loss can significantly hinder an individual's ability to engage socially and, when left untreated, can lead to anxiety, depression, and even dementia. The most common type of hearing loss is sensor-neural hearing loss that is treated using hearing aids (HAs). However, a significant fraction of individuals that may benefit from using HA do not use them and, the satisfaction of those that do, is only around 60%. Today, we have only a limited understanding regarding the factors that contribute to the low adoption and satisfaction rates. This is a limitation of existing laboratory-based assessment methods that cannot accurately predict the performance of HAs in the real-world as they do not fully reproduce the complexities of real-world environments.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355107555Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Mobile Ecological Momentary Assessment for Hearing Aid Evaluation.
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100 1 $a Hasan, Syed Shabih. $3 1181307
245 1 0 $a Mobile Ecological Momentary Assessment for Hearing Aid Evaluation.
264 0 $c 2017
300 $a 1 online resource (153 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.
500 $a Adviser: Octav Chipara.
502 $a Thesis (Ph.D.) $c The University of Iowa $d 2017.
504 $a Includes bibliographical references
520 $a Hearing loss can significantly hinder an individual's ability to engage socially and, when left untreated, can lead to anxiety, depression, and even dementia. The most common type of hearing loss is sensor-neural hearing loss that is treated using hearing aids (HAs). However, a significant fraction of individuals that may benefit from using HA do not use them and, the satisfaction of those that do, is only around 60%. Today, we have only a limited understanding regarding the factors that contribute to the low adoption and satisfaction rates. This is a limitation of existing laboratory-based assessment methods that cannot accurately predict the performance of HAs in the real-world as they do not fully reproduce the complexities of real-world environments.
520 $a There four core contributions of my PhD thesis: i) the development new computer-based methods for assessing HAs in the real-world. Our approach is based on the insight that HA performance is intrinsically dependent on the context in which a HA is used. A context includes characteristics of the listening activity, social context, and acoustic environment. To evaluate this hypothesis, we have developed AudioSense, a system that uses mobile phones to jointly characterize the context of users and the performance of HAs. ii) We provide the first instance of characterization of the auditory lifestyle of hearing aid users, and the relationships that exist between the context and hearing aid outcomes. iii) We utilize the subjective data collected using AudioSense to build novel models that can predict the success of hearing aid prescriptions for new and experienced users. We also quantitatively prove the importance of collecting contextual information for evaluating hearing aids. iv) We use the objective audio data collected with AudioSense to predict contextual information like acoustic activity and noise level. This provides us a way to intelligently infer contextual information automatically and reduce the burden on the study participants.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Audiology. $3 889171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0300
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of Iowa. $b Computer Science. $3 1181308
773 0 $t Dissertation Abstracts International $g 78-12B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10262229 $z click for full text (PQDT)