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

Friedrich, Björn and Lau, Sandra and Elgert, Lena and Bauer, Jürgen M. and Hein, Andreas (2021) A Deep Learning Approach for TUG and SPPB Score Prediction of (Pre-) Frail Older Adults on Real-Life IMU Data. Healthcare, 9 (2). p. 149. ISSN 2227-9032

[thumbnail of healthcare-09-00149.pdf] Text
healthcare-09-00149.pdf - Published Version

Download (1MB)

Abstract

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.

Item Type: Article
Subjects: Journal Eprints > Medical Science
Depositing User: Managing Editor
Date Deposited: 08 Mar 2023 08:08
Last Modified: 22 Jun 2024 08:04
URI: http://repository.journal4submission.com/id/eprint/688

Actions (login required)

View Item
View Item