Kuebert, T. ; Puder, H. ; Koeppl, H. (2021)
Daily Routine Recognition for Hearing Aid Personalization.
In: SN Computer Science, 2 (3)
doi: 10.1007/s42979-021-00538-3
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
This work focuses on daily routine recognition to personalize the hearing aid (HA) configuration for each user. So far, there is only one public data set containing the data of two acceleration sensors taken under unconstrained real-life conditions of one person. Therefore, we create a realistic and extensive data set with seven subjects and a total length of 63449 min. For the recordings, the HA streams the acceleration and audio data to a mobile phone, where the user simultaneously annotates it. This builds the grounds for our comprehensive simulations, where we train a set of classifiers in an offline and online manner to analyze the model generalization abilities across subjects for high-level activities. To achieve this, we build a feature representation, which describes the recurring daily situations and environments well. For the offline classification, the deep neural network, multi-layer perceptron (MLP), and random forest (RF) trained in a person-dependent manner show the significantly best F-measure performance of 86.6%, 87.1%, and 87.3%, respectively. We confirm that for high-level activities the person-dependent model outperforms the independent one. In our online experiments, we personalize a model that was pretrained in a person-independent manner by daily updates. Thereby, multiple incremental learners and an online RF are tested. We demonstrate that MLP and RF improve the F-measure compared to the offline baselines.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Kuebert, T. ; Puder, H. ; Koeppl, H. |
| Art des Eintrags: | Bibliographie |
| Titel: | Daily Routine Recognition for Hearing Aid Personalization |
| Sprache: | Englisch |
| Publikationsjahr: | 11 März 2021 |
| Ort: | Singapore |
| Verlag: | Springer Singapore |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | SN Computer Science |
| Jahrgang/Volume einer Zeitschrift: | 2 |
| (Heft-)Nummer: | 3 |
| Kollation: | 12 Seiten |
| DOI: | 10.1007/s42979-021-00538-3 |
| URL / URN: | https://link.springer.com/article/10.1007/s42979-021-00538-3 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | This work focuses on daily routine recognition to personalize the hearing aid (HA) configuration for each user. So far, there is only one public data set containing the data of two acceleration sensors taken under unconstrained real-life conditions of one person. Therefore, we create a realistic and extensive data set with seven subjects and a total length of 63449 min. For the recordings, the HA streams the acceleration and audio data to a mobile phone, where the user simultaneously annotates it. This builds the grounds for our comprehensive simulations, where we train a set of classifiers in an offline and online manner to analyze the model generalization abilities across subjects for high-level activities. To achieve this, we build a feature representation, which describes the recurring daily situations and environments well. For the offline classification, the deep neural network, multi-layer perceptron (MLP), and random forest (RF) trained in a person-dependent manner show the significantly best F-measure performance of 86.6%, 87.1%, and 87.3%, respectively. We confirm that for high-level activities the person-dependent model outperforms the independent one. In our online experiments, we personalize a model that was pretrained in a person-independent manner by daily updates. Thereby, multiple incremental learners and an online RF are tested. We demonstrate that MLP and RF improve the F-measure compared to the offline baselines. |
| Freie Schlagworte: | Machine learning, Daily routine, Activity recognition, Hearing aid, Sensor fusion |
| ID-Nummer: | Artikel-ID: 133 |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Bioinspirierte Kommunikationssysteme 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik Exzellenzinitiative Exzellenzinitiative > Graduiertenschulen Exzellenzinitiative > Graduiertenschulen > Graduate School of Computational Engineering (CE) |
| Hinterlegungsdatum: | 06 Sep 2021 06:56 |
| Letzte Änderung: | 07 Mär 2024 10:37 |
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| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
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Daily Routine Recognition for Hearing Aid Personalization. (deposited 05 Mär 2024 12:45)
- Daily Routine Recognition for Hearing Aid Personalization. (deposited 06 Sep 2021 06:56) [Gegenwärtig angezeigt]
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