文章基本信息

标题：A Deep Learning Approach for TUG and SPPB Score Prediction of (Pre-) Frail Older Adults on Real-Life IMU Data
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作者：Björn Friedrich ; Sandra Lau ; Lena Elgert 等
期刊名称：Healthcare
电子版ISSN：2227-9032
出版年度：2021
卷号：9
期号：2
页码：149
DOI：10.3390/healthcare9020149
出版社：MDPI Publishing
摘要：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.
关键词：pre-frail; frail; older adults; mobility assessments; machine learning; supervised learning; decision support pre-frail ; frail ; older adults ; mobility assessments ; machine learning ; supervised learning ; decision support