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| Verfasst von: | Brand, Yonatan E. [VerfasserIn]  |
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| | Kluge, Felix [VerfasserIn] |
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| | Palmerini, Luca [VerfasserIn] |
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| | Paraschiv-Ionescu, Anisoara [VerfasserIn] |
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| | Becker, Clemens [VerfasserIn] |
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| | Cereatti, Andrea [VerfasserIn] |
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| | Maetzler, Walter [VerfasserIn] |
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| | Sharrack, Basil [VerfasserIn] |
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| | Vereijken, Beatrix [VerfasserIn] |
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| | Yarnall, Alison J. [VerfasserIn] |
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| | Rochester, Lynn [VerfasserIn] |
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| | Del Din, Silvia [VerfasserIn] |
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| | Muller, Arne [VerfasserIn] |
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| | Buchman, Aron S. [VerfasserIn] |
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| | Hausdorff, Jeffrey M. [VerfasserIn] |
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| | Perlman, Or [VerfasserIn] |
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| Titel: | Self-supervised learning of wrist-worn daily living accelerometer data improves the automated detection of gait in older adults |
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| Verf.angabe: | Yonatan E. Brand, Felix Kluge, Luca Palmerini, Anisoara Paraschiv-Ionescu, Clemens Becker, Andrea Cereatti, Walter Maetzler, Basil Sharrack, Beatrix Vereijken, Alison J. Yarnall, Lynn Rochester, Silvia Del Din, Arne Muller, Aron S. Buchman, Jeffrey M. Hausdorff & Or Perlman |
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| E-Jahr: | 2024 |
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| Jahr: | 06 September 2024 |
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| Umfang: | 15 S. |
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| Illustrationen: | Illustrationen |
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| Fussnoten: | Gesehen am 10.03.2025 |
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| Titel Quelle: | Enthalten in: Scientific reports |
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| Ort Quelle: | [London] : Springer Nature, 2011 |
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| Jahr Quelle: | 2024 |
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| Band/Heft Quelle: | 14(2024), Artikel-ID 20854, Seite 1-15 |
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| ISSN Quelle: | 2045-2322 |
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| Abstract: | Progressive gait impairment is common among aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Here, we developed ElderNet, a self-supervised learning model for gait detection from wrist-worn accelerometer data. Validation involved two diverse cohorts, including over 1000 participants without gait labels, as well as 83 participants with labeled data: older adults with Parkinson's disease, proximal femoral fracture, chronic obstructive pulmonary disease, congestive heart failure, and healthy adults. ElderNet presented high accuracy (96.43 ± 2.27), specificity (98.87 ± 2.15), recall (82.32 ± 11.37), precision (86.69 ± 17.61), and F1 score (82.92 ± 13.39). The suggested method yielded superior performance compared to two state-of-the-art gait detection algorithms, with improved accuracy and F1 score (p < 0.05). In an initial evaluation of construct validity, ElderNet identified differences in estimated daily walking durations across cohorts with different clinical characteristics, such as mobility disability (p < 0.001) and parkinsonism (p < 0.001). The proposed self-supervised method has the potential to serve as a valuable tool for remote phenotyping of gait function during daily living in aging adults, even among those with gait impairments. |
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| DOI: | doi:10.1038/s41598-024-71491-3 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
kostenfrei: Volltext: https://doi.org/10.1038/s41598-024-71491-3 |
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| | kostenfrei: Volltext: https://www.nature.com/articles/s41598-024-71491-3 |
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| | DOI: https://doi.org/10.1038/s41598-024-71491-3 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | Biomedical engineering |
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| | Geriatrics |
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| | Neurological disorders |
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| K10plus-PPN: | 1919439951 |
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| Verknüpfungen: | → Zeitschrift |
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Self-supervised learning of wrist-worn daily living accelerometer data improves the automated detection of gait in older adults / Brand, Yonatan E. [VerfasserIn]; 06 September 2024 (Online-Ressource)
