Comparison of machine learning approaches for near-fall-detection with motion sensors.

Hellmers, Sandra; Krey, Elias; Gashi, Arber; Koschate, Jessica; Schmidt, Laura; Stuckenschneider, Tim; Hein, Andreas; Zieschang, Tania

Hellmers, Sandra; Krey, Elias; Gashi, Arber; Koschate, Jessica; Schmidt, Laura; Stuckenschneider, Tim; Hein, Andreas; Zieschang, Tania.

Afiliação

Hellmers S; Assistance Systems and Medical Device Technology, Department for Health Services Research, Carl von Ossietzky University, Oldenburg, Germany.
Krey E; Assistance Systems and Medical Device Technology, Department for Health Services Research, Carl von Ossietzky University, Oldenburg, Germany.
Gashi A; Geriatric Medicine, Department for Health Services Research, Carl von Ossietzky University, Oldenburg, Germany.
Koschate J; Geriatric Medicine, Department for Health Services Research, Carl von Ossietzky University, Oldenburg, Germany.
Schmidt L; Geriatric Medicine, Department for Health Services Research, Carl von Ossietzky University, Oldenburg, Germany.
Stuckenschneider T; Geriatric Medicine, Department for Health Services Research, Carl von Ossietzky University, Oldenburg, Germany.
Hein A; Assistance Systems and Medical Device Technology, Department for Health Services Research, Carl von Ossietzky University, Oldenburg, Germany.
Zieschang T; Geriatric Medicine, Department for Health Services Research, Carl von Ossietzky University, Oldenburg, Germany.

Front Digit Health ; 5: 1223845, 2023.

Article em En | MEDLINE | ID: mdl-37564882

Palavras-chave

CNN; IMU; fall risk; machine learning; mobile health; near-fall; perturbation

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google