Friedrich Björn, Lau Sandra, Elgert Lena, Bauer Jürgen M, Hein Andreas
Assistance Systems and Medical Device Technology, Carl von Ossietzky University, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany.
Geriatric Medicine, Carl von Ossietzky University, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany.
Healthcare (Basel). 2021 Feb 2;9(2):149. 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.
由于老年人容易出现功能衰退，使用惯性测量单元（IMU）进行活动能力评估分数预测可为医生提供有价值的信息，以便在早期诊断活动能力和身体机能的变化，并增加康复的机会。本研究介绍了一种使用IMU数据和深度神经网络预测定时起立行走测试分数和简短身体机能测试评估分数的方法。该方法在20名平均年龄为84.7岁的体弱或（准）体弱老年人队列的真实数据上得到了验证。对于网络已知的参与者，深度神经网络在这两项测试中均达到了约95%的准确率。
