...
首页> 外文期刊>Sensors >Ambulatory Assessment of Instantaneous Velocity during Walking Using Inertial Sensor Measurements
【24h】

Ambulatory Assessment of Instantaneous Velocity during Walking Using Inertial Sensor Measurements

机译:使用惯性传感器测量的步行过程中动态速度的动态评估

获取原文
           

摘要

A novel approach for estimating the instantaneous velocity of the pelvis during walking was developed based on Inertial Measurement Units (IMUs). The instantaneous velocity was modeled by the sum of a cyclical component, decomposed in the Medio-Lateral (ML), VerTical (VT) and Antero-Posterior (AP) directions, and the Average Progression Velocity (APV) over each gait cycle. The proposed method required the availability of two IMUs, attached to the pelvis and one shank. Gait cycles were identified from the shank angular velocity; for each cycle, the Fourier series coefficients of the pelvis and shank acceleration signals were computed. The cyclical component was estimated by Fourier-based time-integration of the pelvis acceleration. A Bayesian Linear Regression (BLR) with Automatic Relevance Determination (ARD) predicted the APV from the stride time, the stance duration, and the Fourier series coefficients of the shank acceleration. Healthy subjects performed tasks of Treadmill Walking (TW) and Overground Walking (OW), and an optical motion capture system (OMCS) was used as reference for algorithm performance assessment. The widths of the limits of agreements (±1.96 standard deviation) were computed between the proposed method and the reference OMCS, yielding, for the cyclical component in the different directions: ML: ±0.07 m/s (±0.10 m/s); VT: ±0.03 m/s (±0.05 m/s); AP: ±0.06 m/s (±0.10 m/s), in TW (OW) conditions. The ARD-BLR achieved an APV root mean square error of 0.06 m/s (0.07 m/s) in the same conditions.
机译:基于惯性测量单元(IMU),开发了一种新的估算行走过程中骨盆瞬时速度的方法。瞬时速度是由一个周期性分量的总和建模的,该周期性分量在各个步态周期的中,横向(ML),垂直(VT)和前后(AP)方向分解,以及平均前进速度(APV)。所提出的方法需要有两个连接到骨盆和一个小腿的IMU。从小腿角速度确定步态周期。对于每个周期,计算骨盆和小腿加速度信号的傅立叶级数系数​​。周期性分量是通过基于骨盆加速度的傅立叶时间积分来估算的。具有自动相关性确定(ARD)的贝叶斯线性回归(BLR)可从步幅时间,姿势持续时间和小腿加速度的傅立叶级数系数​​预测APV。健康受试者执行了跑步机行走(TW)和地面行走(OW)的任务,并使用光学运动捕捉系统(OMCS)作为算法性能评估的参考。计算出所提议的方法与参考OMCS之间的一致极限的宽度(±1.96标准偏差),得出不同方向上的周期性分量:ML:±0.07 m / s(±0.10 m / s);垂直速度:±0.03 m / s(±0.05 m / s); AP:在TW(OW)条件下为±0.06 m / s(±0.10 m / s)。在相同条件下,ARD-BLR的APV均方根误差为0.06 m / s(0.07 m / s)。

著录项

获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号