Empowering the hearing aid wearer through logging plus learning

Hayes, Donald

doi:10.1097/01.hj.0000338124.37711.cc

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…Early algorithms used information about the wearer's volume control (VC) adjustment patterns to gradually change the default VC setting at power up (e.g., Chalupper 2006;Hayes 2007;Groth et al 2008). However, one potential caveat in this study is that, apart from age, the participants, selected primarily for their capability and willingness to use the body-worn research device in the field, could hardly be considered representative of a typical clinical population.…”

Section: Gitte Keidser and Karima Alamudimentioning

confidence: 99%

Real-Life Efficacy and Reliability of Training a Hearing Aid

Keidser

Alamudi

2013

Ear &Amp; Hearing

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Section: Gitte Keidser and Karima Alamudimentioning

confidence: 99%

Real-Life Efficacy and Reliability of Training a Hearing Aid

Keidser

Alamudi

2013

Ear &Amp; Hearing

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“…Dillon et al (2006) described a trainable hearing aid featuring a learning algorithm that promised several potential advantages over traditional hearing aids, including personalization of parameters in actual listening environments, fewer postfitting visits for fine-tuning, and greater user involvement in the fitting process, resulting in increased sense of ownership and engagement. The first commercial hearing aid with some learning capacity, the Siemens Centra, was equipped with algorithms that used information about the user’s volume control selection patterns to gradually adjust the default volume setting (Chalupper, 2006; Hayes, 2007). In a real-world experiment using Centras, Chalupper (2006) allowed 19 experienced hearing aid wearers to train the volume setting in different environments.…”

Section: Introductionmentioning

confidence: 99%

Smartphone-Based System for Learning and Inferring Hearing Aid Settings

Aldaz¹,

Puria²,

Leifer³

2016

J Am Acad Audiol

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Background Previous research has shown that hearing aid wearers can successfully self-train their instruments’ gain-frequency response and compression parameters in everyday situations. Combining hearing aids with a smartphone introduces additional computing power, memory, and a graphical user interface that may enable greater setting personalization. To explore the benefits of self-training with a smartphone-based hearing system, a parameter space was chosen with four possible combinations of microphone mode (omnidirectional and directional) and noise reduction state (active and off). The baseline for comparison was the “untrained system,” that is, the manufacturer’s algorithm for automatically selecting microphone mode and noise reduction state based on acoustic environment. The “trained system” first learned each individual’s preferences, self-entered via a smartphone in real-world situations, to build a trained model. The system then predicted the optimal setting (among available choices) using an inference engine, which considered the trained model and current context (e.g., sound environment, location, and time). Purpose To develop a smartphone-based prototype hearing system that can be trained to learn preferred user settings. Determine whether user study participants showed a preference for trained over untrained system settings. Research Design An experimental within-participants study. Participants used a prototype hearing system—comprising two hearing aids, Android smartphone, and body-worn gateway device—for ~6 weeks. Study Sample Sixteen adults with mild-to-moderate sensorineural hearing loss (HL) (ten males, six females; mean age = 55.5 yr). Fifteen had ≥6 mo of experience wearing hearing aids, and 14 had previous experience using smartphones. Intervention Participants were fitted and instructed to perform daily comparisons of settings (“listening evaluations”) through a smartphone-based software application called Hearing Aid Learning and Inference Controller (HALIC). In the four-week-long training phase, HALIC recorded individual listening preferences along with sensor data from the smartphone—including environmental sound classification, sound level, and location—to build trained models. In the subsequent two-week-long validation phase, participants performed blinded listening evaluations comparing settings predicted by the trained system (“trained settings”) to those suggested by the hearing aids’ untrained system (“untrained settings”). Data Collection and Analysis We analyzed data collected on the smartphone and hearing aids during the study. We also obtained audiometric and demographic information. Results Overall, the 15 participants with valid data significantly preferred trained settings to untrained settings (paired-samples t test). Seven participants had a significant preference for trained settings, while one had a significant preference for untrained settings (binomial test). The remaining seven participants had nonsignificant preferences. Pooling data across participants...

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“…Like cell phones, some hearing aids now incorporate selflearning features. For example, the hearing aid monitors when the user adjusts the volume control and after a certain period of time, the hearing aid automatically adjusts itself to the average level where the user typically sets the volume control (Hayes, 2007). Some hearing aids will also remember where the patient had his or her volume set and increase the volume by a small number of decibels until it eventually reaches a preset target that was established at the time of the hearing aid fitting.…”

Section: Self-learning and Adaptation/acceptance Managersmentioning

confidence: 99%

Advances in Hearing Aid Technology

Kerckhoff¹,

Listenberger²,

Valente³

2008

CICSD

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Digital signal processing (DSP) has provided significant advances and improvements in hearing aid technology. These advances, and the increased processing speed that DSP offers, have resulted in improved and expanded features in current hearing aid technology. These advances have also served to expand the fitting range of amplification for patients who previously could not benefit from amplification. This article reviews the advances and supporting research in specific hearing aid features, devices, and assistive technology developments that allow a greater number of patients access to amplification. These features and devices include directional microphones, Bluetooth and other wireless technology, digital noise reduction, devices for patients with single-sided deafness, frequency transposition, selflearning and adaptation managers, and integrated real ear measures. Despite the changes in hearing aids created by DSP, limitations still exist. Additionally, peer-reviewed research is not yet available to support the reported benefits of some of these advances.

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Empowering the hearing aid wearer through logging plus learning

Cited by 4 publications

References 6 publications

Real-Life Efficacy and Reliability of Training a Hearing Aid

Real-Life Efficacy and Reliability of Training a Hearing Aid

Smartphone-Based System for Learning and Inferring Hearing Aid Settings

Advances in Hearing Aid Technology

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