Walking-speed estimation using a single inertial measurement unit for the older adults
Walking-speed estimation using a single inertial measurement unit for the older adults
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DOI：
10.1371/journal.pone.0227075
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发表时间：
2019-12-26
期刊：
影响因子：
3.7
通讯作者：
Kim, Ki Woong
中科院分区：
文献类型：
--
作者：
Byun, Seonjeong;Lee, Hyang Jun;Kim, Ki Woong
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个生命体征，但很少有使用惯性测量单元（IMU）的步行速度估计算法在老年人，特别是在慢速老年人中得到推导和测试。我们的目标是开发一种基于IMU的老年人步行速度估计算法。方法：我们使用来自队列研究的785名老年人中的659名的数据。我们使用连接在下背部的IMU测量步态，同时参与者以舒适的速度在28米长的圆形人行道上行走三次。使用选定的人口统计学、人体测量学和IMU特征建立最佳拟合线性回归模型来估计步行速度。采用独立验证集的平均绝对误差（MAE）和均方根误差（RMSE）验证算法的准确性。此外，我们使用类内相关系数（ICCs）验证了GAITRite的并发效度。结果该算法结合了从IMU传感器获得的年龄、性别、脚长、垂直位移、节奏和步长变异性。与GAITRite相比，该方法对老年人步行速度的估计精度较高，并发效度显著（MAE = 4.70%, RMSE = 6.81 cm/s， ICC(3,1)） = 0.937)。此外，通过在一般回归模型估计后依次应用慢速特定的回归模型，即使对慢速行走也能获得较高的估计精度。该模型的精度高于基于人类步态模型的模型，无论是否校准以拟合总体。结论基于惯性传感器的步行速度估计算法能够准确估计老年人的步行速度。
BackgroundAlthough 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.MethodsWe 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).ResultsThe 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.ConclusionsThe developed inertial-sensor-based walking-speed estimation algorithm can accurately estimate the walking speed of older adults.