Modern hearing aids are not simple passive sound enhancers, but rather complex devices that can log (via smartphones) multivariate real-time data from the acoustic environment of a user. In the evotion project (http://h2020evotion.eu) such hearing aids are integrated with a Big Data analytics platform to bring about ecologically valid evidence to support the hearing healthcare sector. Here, we present the background of the Big Data analytics platform and demonstrate that modeling of longitudinally sampled data from hearing aids can support clinical investigations with hypotheses about hearing aid usage prognosis, and support public health decision-making within the hearing healthcare sector by simulation techniques. We found, that distinct characteristics of the acoustic environment significantly modulate how hearing impaired individuals use their hearing aids. Higher sound levels and an increased sound diversity but degraded signal quality all predicts more minutes of use per hour. By simulation, we show that a projected increase in the overall sound levels by 10dB followed by a 4dB increase in noise exposure will increase the need for hearing aid use by an additional 1 hour/day across a population of hearing impaired hearing aid users.
Improving hearing healthcare with big data analytics of real-time hearing aid data / J.H. Christensen, N.H. Pontoppidan, M. Anisetti, V. Bellandi, M. Cremonini (PROCEEDINGS IEEE WORLD CONGRESS SERVICES). - In: 2019 IEEE World Congress on Services (SERVICES)[s.l] : IEEE, 2019. - ISBN 9781728138510. - pp. 307-313 (( convegno World Congress on Services tenutosi a Milano nel 2019.
|Titolo:||Improving hearing healthcare with big data analytics of real-time hearing aid data|
|Parole Chiave:||hearing aids; Big Data analytics; mixed models; multilevel clustered data; evidence-based public-health policies|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
|Progetto:||EVidenced based management of hearing impairments: Public health p?licy making based on fusing big data analytics and simulaTION|
|Data di pubblicazione:||2019|
|Enti collegati al convegno:||IEEE|
IEEE Computer Society (CS)
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1109/SERVICES.2019.00086|
|Tipologia:||Book Part (author)|
|Appare nelle tipologie:||03 - Contributo in volume|