Smartphone-Based System for Learning and Inferring Hearing Aid Settings

Abstract

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...

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

“…We found that program P1 was preferred on average 66% of the time. This was significantly different from previous findings of respectively 33% [20] and 37% [34]. This could be due to this being the default program, or more likely, that it fulfilled the needs in most contexts by providing an omnidirectional frontal focus mimicking the natural dampening of sounds from behind and from the sides, caused by the shape of the ears and the shadowing effect of the head.…”

“…Instead, aiming to infer preferences by connecting directly to users through their smartphones, Aldaz et al investigated the feasibility of using machine learning to predict the optimal settings, on the basis of the signal-to-noise ratio (SNR) and attenuation for the hearing aids. They found that half of the test subjects preferred the personalized settings [20]. Other attempts at using machine learning to optimize hearing aids have shown similar findings [21,22].…”

Compare the Level of Satisfaction Among Smart Phone and Traditional Hearing Aids Users

“…The conclusion specifies that satisfaction levels in contributors using smart phone hearing aids were extremely significant as compared to the participants using traditional hearing aids. While the majority reported in previous researches shows that the satisfaction with the hearing aids increased when it provides smart phone hearing aids to the participants using hearing aids 21 .SADL (Satisfaction with Amplification in Daily Life) a standard questionnaire which was used to compare the satisfaction level between the smart phone hearing aids users and traditional hearing aids users 22 .A study was conducted by AM Amlani, B Taylor, C Levy as Utility of smartphone-based hearing aid applications as a substitute to traditional hearing aids and there results indicates that the satisfaction level in smart phone hearing aids users was more than traditional hearing aids users 23 .Similarly,in our study the level of satisfaction among smart phone hearing aids users was more as compared to traditional hearing aids users. A study was conducted in January 2010 by Kochkin, Sergei as Marke Trak VIII Consumer satisfaction with hearing aids is slowly increasing 24 .Digital and smart phone hearing aids have more benefits according to satisfaction level 25 .…”

Indoor Localization Services for Hearing Aids using Bluetooth Low Energy

“…As mentioned before, the sampling ratio could also be increased. However, this would lead to the higher energy consumption, and processing power requirements for the hearing aids which as shown in the studies [2,5,27] shall be limited. Figure 10 shows a clear distinction between the RSSI readings corresponding to the three meters, and two meters distances between the devices, which is in compliance with the RSSI propagation model proposed by [1].…”