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

• Published in: Healthcare (Basel) Vol. 9; no. 2; p. 149
• Main Authors: Friedrich, Björn, Lau, Sandra, Elgert, Lena, Bauer, Jürgen M, Hein, Andreas
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 02-02-2021; MDPI
• Subjects: Accuracy; Deep learning; frail; Frailty; Gait; Geriatrics; Machine learning; Mobility; mobility assessments; Neural networks; older adults; Older people; Parameter estimation; pre-frail; Professionals; Sensors; supervised learning; pre-frail; supervised learning; mobility assessments; older adults; frail; decision support; machine learning
• ISSN: 2227-9032; 2227-9032
• DOI: 10.3390/healthcare9020149

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.