Wearable Sensor-Based Prediction Model of Timed up and Go Test in Older Adults.
Wearable Sensor-Based Prediction Model of Timed up and Go Test in Older Adults.
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DOI：
10.3390/s21206831
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发表时间：
2021-10-14
期刊：
影响因子：
--
通讯作者：
Youn JH
中科院分区：
文献类型：
--
作者：
Choi J;Parker SM;Knarr BA;Gwon Y;Youn JH
定时起跳（TUG）测试经常被用于评估老年人跌倒的风险，因为它是一种简单、快速、简单的检查功能活动和平衡的方法，无需特殊设备。本研究的目的是开发一个模型，利用从可穿戴传感器收集的正常行走时的三维加速度数据来预测TUG测试。我们招募了37名老年人进行户外步行任务，并在每个参与者身上安装了7个基于惯性测量单元（IMU）的传感器。采用弹性网和脊回归方法减少步态特征集，建立预测模型。所提出的预测模型在较小的预测误差范围内可靠地估计了参与者的TUG分数。尽管两个足部传感器的预测精度略好于其他配置(例如MAPE: foot (0.865 s) > foot （0.918 s) >骨盆（0.921 s）），但我们建议在骨盆处使用单个IMU传感器，因为它可以提供佩戴舒适性，同时避免日常活动的干扰。提出的预测模型可以使临床医生通过评估老年人日常行走时的TUG评分来远程评估老年人的跌倒风险。
The Timed Up and Go (TUG) test has been frequently used to assess the risk of falls in older adults because it is an easy, fast, and simple method of examining functional mobility and balance without special equipment. The purpose of this study is to develop a model that predicts the TUG test using three-dimensional acceleration data collected from wearable sensors during normal walking. We recruited 37 older adults for an outdoor walking task, and seven inertial measurement unit (IMU)-based sensors were attached to each participant. The elastic net and ridge regression methods were used to reduce gait feature sets and build a predictive model. The proposed predictive model reliably estimated the participants’ TUG scores with a small margin of prediction errors. Although the prediction accuracies with two foot-sensors were slightly better than those of other configurations (e.g., MAPE: foot (0.865 s) > foot and pelvis (0.918 s) > pelvis (0.921 s)), we recommend the use of a single IMU sensor at the pelvis since it would provide wearing comfort while avoiding the disturbance of daily activities. The proposed predictive model can enable clinicians to assess older adults’ fall risks remotely through the evaluation of the TUG score during their daily walking.
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影响因子：
2.9
作者：
Castell, Maria-Victoria;Sanchez, Mercedes;Otero, Angel
通讯作者：
Otero, Angel
DOI：
10.3390/biomimetics6020022
发表时间：
2021-03-26
期刊：
Biomimetics (Basel, Switzerland)
影响因子：
--
作者：
Di Gregorio R;Vocenas L
通讯作者：
Vocenas L
影响因子：
2.2
作者：
Chiu, AYY;Au-Yeung, SSY;Lo, SK
通讯作者：
Lo, SK
影响因子：
2.5
作者：
HOERL, AE;KENNARD, RW
通讯作者：
KENNARD, RW
影响因子：
2.5
作者：
GOLUB, GH;HEATH, M;WAHBA, G
通讯作者：
WAHBA, G