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Mobile Ecological Momentary Assessment for Hearing Aid Evaluation

机译:用于助听器评估的移动式生态矩评估

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摘要

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.;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.
机译:听力下降会严重阻碍个人的社交能力,如果不及时治疗,则可能导致焦虑,沮丧甚至痴呆。听力损失最常见的类型是使用助听器(HAs)治疗的传感器神经性听力损失。但是,可以从使用HA中受益的很大一部分人不使用它们,对这样做的人的满意度仅为60%左右。如今,对于导致较低的采用率和满意度的因素,我们只有很少的了解。这是现有的基于实验室的评估方法的局限性,因为它们无法完全再现现实环境的复杂性,因此无法准确预测HA在现实世界中的表现。;我的博士学位论文有四个核心贡献:i)开发新的基于计算机的方法来评估现实世界中的HA。我们的方法基于这样的见解,即HA性能本质上取决于使用HA的上下文。上下文包括听力活动,社交上下文和声学环境的特征。为了评估该假设,我们开发了AudioSense,这是一个使用手机共同表征用户环境和HA性能的系统。 ii)我们提供了表征助听器用户听觉生活方式的第一个实例,以及上下文和助听器结果之间存在的关系。 iii)我们利用通过AudioSense收集的主观数据来构建新颖的模型,该模型可以预测新手和有经验用户的助听器处方是否成功。我们还定量证明了收集上下文信息对评估助听器的重要性。 iv)我们使用通过AudioSense收集的客观音频数据来预测上下文信息,例如声学活动和噪声水平。这为我们提供了一种自动智能地推断上下文信息并减轻研究参与者负担的方法。

著录项

  • 作者

    Hasan, Syed Shabih.;

  • 作者单位

    The University of Iowa.;

  • 授予单位 The University of Iowa.;
  • 学科 Computer science.;Audiology.
  • 学位 Ph.D.
  • 年度 2017
  • 页码 153 p.
  • 总页数 153
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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