Department of Psychiatry, Seoul National University, College of Medicine, Seoul, Korea.
Department of Neuropsychiatry, National Medical Center, Seoul, Korea.
PLoS One. 2019 Dec 26;14(12):e0227075. doi: 10.1371/journal.pone.0227075. eCollection 2019.
Although walking speed is associated with important clinical outcomes and designated as the sixth vital sign of the elderly, few walking-speed estimation algorithms using an inertial measurement unit (IMU) have been derived and tested in the older adults, especially in the elderly with slow speed. We aimed to develop a walking-speed estimation algorithm for older adults based on an IMU.
We used data from 659 of 785 elderly enrolled from the cohort study. We measured gait using an IMU attached on the lower back while participants walked around a 28 m long round walkway thrice at comfortable paces. Best-fit linear regression models were developed using selected demographic, anthropometric, and IMU features to estimate the walking speed. The accuracy of the algorithm was verified using mean absolute error (MAE) and root mean square error (RMSE) in an independent validation set. Additionally, we verified concurrent validity with GAITRite using intraclass correlation coefficients (ICCs).
The proposed algorithm incorporates the age, sex, foot length, vertical displacement, cadence, and step-time variability obtained from an IMU sensor. It exhibited high estimation accuracy for the walking speed of the elderly and remarkable concurrent validity compared to the GAITRite (MAE = 4.70%, RMSE = 6.81 𝑐𝑚/𝑠, concurrent validity (ICC (3,1)) = 0.937). Moreover, it achieved high estimation accuracy even for slow walking by applying a slow-speed-specific regression model sequentially after estimation by a general regression model. The accuracy was higher than those obtained with models based on the human gait model with or without calibration to fit the population.
The developed inertial-sensor-based walking-speed estimation algorithm can accurately estimate the walking speed of older adults.
尽管步行速度与重要的临床结果相关，并被指定为老年人的第六生命体征，但很少有基于惯性测量单元（IMU）的步行速度估计算法在老年人中得到推导和测试，尤其是在速度较慢的老年人中。我们旨在开发一种基于 IMU 的老年人步行速度估计算法。
我们使用了来自队列研究中 785 名老年人中的 659 名的数据。当参与者以舒适的速度在 28 米长的环形步道上走三圈时，我们使用附着在腰部的 IMU 测量步态。使用选定的人口统计学、人体测量学和 IMU 特征的最佳拟合线性回归模型来估计步行速度。在独立验证集中，通过平均绝对误差（MAE）和均方根误差（RMSE）验证算法的准确性。此外，我们使用组内相关系数（ICC）验证与 GAITRite 的同时效度。
所提出的算法结合了从 IMU 传感器获得的年龄、性别、脚长、垂直位移、步频和步时变异性。它对老年人的步行速度具有很高的估计准确性，与 GAITRite 相比具有显著的同时效度（MAE=4.70%，RMSE=6.81 厘米/秒，同时效度（ICC（3,1））=0.937）。此外，它通过在一般回归模型估计后顺序应用特定于慢速的回归模型，即使在慢速行走时也能实现高精度估计。其准确性高于基于人体步态模型的模型或不进行人群校准的模型获得的准确性。
开发的基于惯性传感器的步行速度估计算法可以准确估计老年人的步行速度。
