A Deep Learning Approach for TUG and SPPB Score Prediction of (Pre-) Frail Older Adults on Real-Life IMU Data

Since older adults are prone to functional decline, using Inertial-Measurement-Units (IMU) for mobility assessment score prediction gives valuable information to physicians to diagnose changes in mobility and physical performance at an early stage and increases the chances of rehabilitation. This re...

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Autores principales: Friedrich, Björn, Lau, Sandra, Elgert, Lena, Bauer, Jürgen M., Hein, Andreas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7912931/
https://www.ncbi.nlm.nih.gov/pubmed/33540555
http://dx.doi.org/10.3390/healthcare9020149
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author Friedrich, Björn
Lau, Sandra
Elgert, Lena
Bauer, Jürgen M.
Hein, Andreas
author_facet Friedrich, Björn
Lau, Sandra
Elgert, Lena
Bauer, Jürgen M.
Hein, Andreas
author_sort Friedrich, Björn
collection PubMed
description Since older adults are prone to functional decline, using Inertial-Measurement-Units (IMU) for mobility assessment score prediction gives valuable information to physicians to diagnose changes in mobility and physical performance at an early stage and increases the chances of rehabilitation. This research introduces an approach for predicting the score of the Timed Up & Go test and Short-Physical-Performance-Battery assessment using IMU data and deep neural networks. The approach is validated on real-world data of a cohort of 20 frail or (pre-) frail older adults of an average of 84.7 years. The deep neural networks achieve an accuracy of about 95% for both tests for participants known by the network.
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spelling pubmed-79129312021-02-28 A Deep Learning Approach for TUG and SPPB Score Prediction of (Pre-) Frail Older Adults on Real-Life IMU Data Friedrich, Björn Lau, Sandra Elgert, Lena Bauer, Jürgen M. Hein, Andreas Healthcare (Basel) Article Since older adults are prone to functional decline, using Inertial-Measurement-Units (IMU) for mobility assessment score prediction gives valuable information to physicians to diagnose changes in mobility and physical performance at an early stage and increases the chances of rehabilitation. This research introduces an approach for predicting the score of the Timed Up & Go test and Short-Physical-Performance-Battery assessment using IMU data and deep neural networks. The approach is validated on real-world data of a cohort of 20 frail or (pre-) frail older adults of an average of 84.7 years. The deep neural networks achieve an accuracy of about 95% for both tests for participants known by the network. MDPI 2021-02-02 /pmc/articles/PMC7912931/ /pubmed/33540555 http://dx.doi.org/10.3390/healthcare9020149 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Friedrich, Björn
Lau, Sandra
Elgert, Lena
Bauer, Jürgen M.
Hein, Andreas
A Deep Learning Approach for TUG and SPPB Score Prediction of (Pre-) Frail Older Adults on Real-Life IMU Data
title A Deep Learning Approach for TUG and SPPB Score Prediction of (Pre-) Frail Older Adults on Real-Life IMU Data
title_full A Deep Learning Approach for TUG and SPPB Score Prediction of (Pre-) Frail Older Adults on Real-Life IMU Data
title_fullStr A Deep Learning Approach for TUG and SPPB Score Prediction of (Pre-) Frail Older Adults on Real-Life IMU Data
title_full_unstemmed A Deep Learning Approach for TUG and SPPB Score Prediction of (Pre-) Frail Older Adults on Real-Life IMU Data
title_short A Deep Learning Approach for TUG and SPPB Score Prediction of (Pre-) Frail Older Adults on Real-Life IMU Data
title_sort deep learning approach for tug and sppb score prediction of (pre-) frail older adults on real-life imu data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7912931/
https://www.ncbi.nlm.nih.gov/pubmed/33540555
http://dx.doi.org/10.3390/healthcare9020149
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