A Deep Learning Approach for TUG and SPPB Score Prediction of (Pre-) Frail Older Adults on Real-Life IMU Data.
作者： Andreas Hein , Jürgen M Bauer , Sandra Lau , Lena Elgert , Björn Friedrich
DOI: 10.3390/HEALTHCARE9020149
关键词:
摘要: Since older adults are prone to functional decline, using Inertial-Measurement-Units (IMU) for mobility assessment score prediction gives valuable information physicians diagnose changes in and physical performance at an early stage increases the chances of rehabilitation. This research introduces approach predicting Timed Up & Go test Short-Physical-Performance-Battery IMU data deep neural networks. The is validated on real-world a cohort 20 frail or (pre-) average 84.7 years. networks achieve accuracy about 95% both tests participants known by network.
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scilit.net参考文章(34)
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