Modern hearing aids (HAs) are not simple passive sound enhancers, but rather complex devices that can log (via smart-phones) multivariate real-time data from the acoustic environment of a user. In the EVOTION project (www.h2020evotion.eu) such hearing aids are integrated with a Big Data analytics (BDA) platform to bring about ecologically valid evidence for policy-making within the hearing healthcare sector. Here, we present the background of the BDA platform and a concrete case study of how longitudinally sampled data from HAs can 1) support hypotheses about HA usage prognosis, and 2) bring new knowledge of how HAs are used across a typical day. In five participants, we found that the hourly HA usage was negatively associated with the variance of the signal-to-noise ratio, and that the daily total HA usage was associated with higher and more diverse sound levels.
Big Data Analytics in Healthcare: Design and Implementation for a Hearing Aid Case / M. Cremonini, J.H. Christensen, M.K. Petersen, N.H. Pontoppidan - In: 2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) / [a cura di] G.S Di Baja, L. Gallo, K. Yetongnon, A. Dipanda, M. Castrillon Santana, R. Chbeir. - [s.l] : IEEE, 2019 May 06. - ISBN 9781538693858. - pp. 296-303 (( Intervento presentato al 14. convegno International Conference on Signal Image Technology & Internet based Systems (SITIS 2018) tenutosi a Las Palmas de Gran Canaria nel 2018 [10.1109/SITIS.2018.00052].
Big Data Analytics in Healthcare: Design and Implementation for a Hearing Aid Case
M. CremoniniUltimo
Membro del Collaboration Group
;
2019
Abstract
Modern hearing aids (HAs) are not simple passive sound enhancers, but rather complex devices that can log (via smart-phones) multivariate real-time data from the acoustic environment of a user. In the EVOTION project (www.h2020evotion.eu) such hearing aids are integrated with a Big Data analytics (BDA) platform to bring about ecologically valid evidence for policy-making within the hearing healthcare sector. Here, we present the background of the BDA platform and a concrete case study of how longitudinally sampled data from HAs can 1) support hypotheses about HA usage prognosis, and 2) bring new knowledge of how HAs are used across a typical day. In five participants, we found that the hourly HA usage was negatively associated with the variance of the signal-to-noise ratio, and that the daily total HA usage was associated with higher and more diverse sound levels.
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