Department of Architecture and Civil Engineering, University of Bath, Claverton Down, Bath BA2 7AY, UK.
Department of Civil & Structural Engineering, INSIGNEO Institute for In-Silico Medicine, University of Sheffield, Sheffield S1 3JD, UK.
Sensors (Basel). 2018 Jun 19;18(6):1966. doi: 10.3390/s18061966.
Continuous monitoring of natural human gait in real-life environments is essential in many applications including disease monitoring, rehabilitation, and professional sports. Wearable inertial measurement units are successfully used to measure body kinematics in real-life environments and to estimate total walking ground reaction forces GRF(t) using equations of motion. However, for inverse dynamics and clinical gait analysis, the GRF(t) of each foot is required separately. Using an experimental dataset of 1243 tri-axial separate-foot GRF(t) time histories measured by the authors across eight years, this study proposes the 'Twin Polynomial Method' (TPM) to estimate the tri-axial left and right foot GRF(t) signals from the total GRF(t) signals. For each gait cycle, TPM fits polynomials of degree five, eight, and nine to the known single-support part of the left and right foot vertical, anterior-posterior, and medial-lateral GRF(t) signals, respectively, to extrapolate the unknown double-support parts of the corresponding GRF(t) signals. Validation of the proposed method both with force plate measurements (gold standard) in the laboratory, and in real-life environment showed a peak-to-peak normalized root mean square error of less than 2.5%, 6.5% and 7.5% for the estimated GRF(t) signals in the vertical, anterior-posterior and medial-lateral directions, respectively. These values show considerable improvement compared with the currently available GRF(t) decomposition methods in the literature.
在许多应用中,包括疾病监测、康复和专业运动,连续监测真实环境中的自然人类步态至关重要。可穿戴惯性测量单元成功地用于测量真实环境中的身体运动学,并使用运动方程估计总步行地面反作用力 GRF(t)。然而,对于逆动力学和临床步态分析,需要分别单独估计每只脚的 GRF(t)。
本研究使用作者在八年期间测量的 1243 个三轴分离脚 GRF(t)时间历史的实验数据集,提出了“双多项式方法”(TPM),从总 GRF(t)信号中估计三轴左、右脚 GRF(t)信号。对于每个步态周期,TPM 将五次、八次和九次多项式拟合到已知的单支撑部分的左、右脚垂直、前后和内外 GRF(t)信号,以推断相应 GRF(t)信号的未知双支撑部分。
实验室中使用力板测量(黄金标准)和真实环境对所提出方法进行验证,结果表明,在垂直、前后和内外方向上,估计的 GRF(t)信号的峰峰值归一化均方根误差分别小于 2.5%、6.5%和 7.5%。与文献中现有的 GRF(t)分解方法相比,这些值有了显著的提高。