College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA.
Department of Biomechanics, University of Nebraska at Omaha, Omaha, NE 68182, USA.
Sensors (Basel). 2021 Oct 14;21(20):6831. doi: 10.3390/s21206831.
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.
计时起立行走（TUG）测试常用于评估老年人跌倒的风险，因为它是一种简单、快速、简单的方法，无需特殊设备即可检查功能移动性和平衡。本研究的目的是开发一种使用可穿戴传感器在正常行走过程中收集的三维加速度数据来预测 TUG 测试的模型。我们招募了 37 名老年人进行户外行走任务，每个参与者都附有七个基于惯性测量单元（IMU）的传感器。使用弹性网和脊回归方法来减少步态特征集并构建预测模型。该预测模型能够可靠地估计参与者的 TUG 得分，预测误差很小。虽然使用两个脚部传感器的预测精度略高于其他配置（例如，平均绝对百分比误差：脚部（0.865 s）>脚部和骨盆（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，因为它可以在避免日常活动干扰的同时提供佩戴舒适性。该预测模型可以使临床医生通过在日常行走期间评估 TUG 得分来远程评估老年人的跌倒风险。
