Insole-Based Estimation of Vertical Ground Reaction Force Using One-Step Learning With Probabilistic Regression and Data Augmentation
Insole-Based Estimation of Vertical Ground Reaction Force Using One-Step Learning With Probabilistic Regression and Data Augmentation
复制标题
DOI：
10.1109/tnsre.2019.2916476
复制
发表时间：
2019-06-01
影响因子：
4.9
通讯作者：
Takahashi, Masaki
中科院分区：
文献类型：
--
作者：
Eguchi, Ryo;Takahashi, Masaki
提出了一种基于鞋垫的垂直地面反作用力（vGRF）的估计作为替代昂贵的力板用于评估病理步态。然而，用于估计的机器学习技术仍然依赖于使用测力板。此外，测量多个步行步数以防止过度拟合引起跌倒风险并对患者造成身体负担。因此，本文提出了一种方便和有效的学习计划的鞋垫为基础的估计vGRF。在这个系统中，我们采用了一个低成本的规模作为替代力板。然后，我们使用高斯过程回归（GPR）来学习模型，以估计vGRF，而不会过度拟合被测量误差和设备噪声破坏的小规模数据集。此外，我们提出了一种基于概率数据增强的“一步学习”方案。这种方法通过考虑它们在步骤之间的典型变化性，将最小（仅一个）步行步骤的实际测量结果增加到多个步骤的虚拟数据集。在实验中，从两个步行步骤学习的GPR模型估计vGRF，对于整个/局部幅度的平均误差为8%或更低。此外，从一个步骤的学习与概率增强提高了估计精度。
An insole-based estimation of the vertical ground reaction force (vGRF) is proposed as an alternative to costly force plates for the evaluation of pathological gait. However, machine learning techniques for estimation still rely on the use of force plates. Moreover, measuring plural walking steps in order to prevent overfitting induces fall risks and physically taxes the patients. Therefore, this paper presents an accessible and efficient learning scheme for the insole-based estimation of vGRF. In this system, we employ a low-cost scale as an alternative to force plates. Then, we use Gaussian process regression (GPR) to learn a model in order to estimate vGRF without overfitting of small-sized data sets corrupted by measurement errors and noise of the devices. In addition, we propose a "one-step learning" scheme based on a probabilistic data augmentation. This approach augments actual measurements of a minimum (just one) walking step to a virtual data set for plural steps by considering their typical variability between steps. In experiments, the GPR models learned from two walking steps estimated vGRF with mean errors of 8% or under for entire/local magnitudes. Moreover, the learning from one step with probabilistic augmentation enhanced the estimation accuracy.