Personalizing the Fitting of Hearing Aids by Learning Contextual Preferences From Internet of Things Data
1.1. The Growing Societal and Personal Costs of Hearing Loss
1.2. The Lack of Personalization in Hearing Health Care
1.3. Learning Preferences From User Behavior
1.4. Making User-Generated Data an Essential Part of Hearing Health Care
2. Materials and Method
- Subject 1
- worked in construction. This subject had a dynamic work environment including noisy construction sites, quiet meeting rooms and driving in between.
- Subject 2
- worked in the transportation sector as a bus driver. This subject was exposed to a constant noise level while at work. The subject retired half-way through the experiment. The subject returned to work in the last month of the experiment, only part-time.
- Subject 3
- worked in an office environment. This subject attended many meetings, including teleconferences. The subject reported that the acoustics in the canteen at work were poor. This subject had many international travels, spending time primarily on flights.
- Subject 4
- was retired. The subject spent several days a week playing cards, with a high noise level and several competing talkers. The subject lived an active life, including activities such as sailing, and was exposed to various sound environments.
- Subject 5
- worked in an office environment. The subject had many meetings in or out of the office, experiencing multiple auditory environments during weekdays.
- Subject 6
- worked in the naval industry, restoring boats and supervising team-building events on sailboats. This subject was subjected to heavy noise exposure from power tools, as well as engine and wind noise. The subject tended to wear the hearing aids when the noise was acceptable or otherwise was not obscured by hearing protection.
- Resembling an omnidirectional perception with a frontal focus. Sounds from the sides and behind the listener are slightly suppressed to resemble the dampening effect due to the shape of the head and the pinna.
- Similar to P1 but gently attenuating directional noise and removal of diffuse noise when encountering complex listening environments.
- Similar to P1 but increasingly attenuates directional noise even in simple listening environments. Has less amplification in mid and high frequencies, producing a “rounder” or “softer” sound. Provides the highest amount of diffuse noise reduction.
- Similar to P3 with even lower thresholds for attenuation of directional noise and diffuse noise removal in all listening environments.
- Identical to P3 with regard to high attenuation and high noise reduction. Has added amplification in mid and high frequencies to provide a brighter sound.
- Similar to P4 with high attenuation. However, no noise reduction is applied. Has an increased amplification in mid to high frequencies, producing a brighter sound.
3.1. Behavioral Patterns Inferred from User-Initiated Program and Volume Changes
3.2. Unique Patterns Characterized by Program Changes
3.3. Alternating Between Omnidirectional and Frontal Focus
3.4. Active and Habitual Users
3.5. Alternating and Unique Patterns
3.6. Weekdays versus Weekends
3.7. Unique Behavioral Patterns over Weeks, and within Weeks
3.8. Unique Patterns Characterized by Volume Change
3.9. Number of Program and Volume Interactions
3.10. Volume Interactions With Respect to Programs
4.1. The Opportunity for Personalizing Hearing Health Care as hearing aids Become Internet of Things Devices
“The hearing aid user comes in for a refitting in the middle of the week. I ask, ’Recall a situation where the hearing aids did not perform as you wanted it to’. The patient thinks, and comes up with, ’Well, yeah, I don’t remember that much, but Monday I had an episode.’I then have to guess what is the essence of this episode, and try to refit the hearing aids to better accommodate similar situations in the future. However, I face several challenges. One is that the users rarely recall episodes, unless they are significant. If it’s a compliant user, they may be writing notes. The second happens only in rare cases. Furthermore, I have to guess what’s needed to be tuned to give a better experience. All of this is based on memory recall and heuristics”.
4.2. One Size Does Not Fit All
4.3. Involvement and Engagement May Lead to a Higher Satisfaction
4.4. The Next Steps to Create Better Hearing Experiences
Conflicts of Interest
Appendix A. Study data
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|Subject||Age Group||Hearing Loss||Experience with OPN||Occupation|
|Subject 1||Subject 2||Subject 3||Subject 4||Subject 5|
|Total usage time (h)||486.25||1189.90||255.78||373.32||551.62|
|P1||P2||P3||P4||P5||P6||Average Per Hour|
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Johansen, B.; Petersen, M.K.; Korzepa, M.J.; Larsen, J.; Pontoppidan, N.H.; Larsen, J.E. Personalizing the Fitting of Hearing Aids by Learning Contextual Preferences From Internet of Things Data. Computers 2018, 7, 1. https://doi.org/10.3390/computers7010001
Johansen B, Petersen MK, Korzepa MJ, Larsen J, Pontoppidan NH, Larsen JE. Personalizing the Fitting of Hearing Aids by Learning Contextual Preferences From Internet of Things Data. Computers. 2018; 7(1):1. https://doi.org/10.3390/computers7010001Chicago/Turabian Style
Johansen, Benjamin, Michael Kai Petersen, Maciej Jan Korzepa, Jan Larsen, Niels Henrik Pontoppidan, and Jakob Eg Larsen. 2018. "Personalizing the Fitting of Hearing Aids by Learning Contextual Preferences From Internet of Things Data" Computers 7, no. 1: 1. https://doi.org/10.3390/computers7010001